biosc - diagnostics: isochrones and $A(Li)$ model comparison¶
string = 'work'
## to change directory:
## string_directory = 'path...'
from matplotlib.path import Path
import pymc as pm
import arviz as az
import bambi as bmb
import xarray as xr
import biosc
import biosc.preprocessing
import matplotlib.ticker as ticker
from pymc import HalfCauchy, Model, Normal, sample
import os
import matplotlib.cm as cm
from netCDF4 import Dataset as NetCDFFile
from scipy.stats import gaussian_kde
from biosc.preprocessing import Preprocessing
from biosc.bhm import BayesianModel
import models_test
Jmag_lbda = 12350.00
Hmag_lbda = 16620.00
Kmag_lbda = 21590.00
BP_lbda = 5109.71
G_lbda = 6217.59
RP_lbda = 7769.02
gmag_lbda = 4849.11
rmag_lbda = 6201.20
wmag_lbda = 6285.91
imag_lbda = 7534.96
zmag_lbda = 8674.20
ymag_lbda = 9627.79
import models_test
from models_test import plt, np, pd, pc, select_nearest_age
path_all = pc(string)
## to change directory:
## path_all = pc_other(string_directory)
import sys
sys.path.append(path_all)
import bmp
from bmp import BayesianModelPlots
path_all
'/pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/'
plt.rcParams.update({'font.size': 14, 'axes.linewidth': 1, 'axes.edgecolor': 'k'})
plt.rcParams['font.family'] = 'serif'
Pleiades data¶
from models_test import PleiadesData
path_data = path_all + 'data/Pleiades_DANCe+GDR3+2MASS+PanSTARRS1+A_Li+Lbol.csv'
pleiades_data = PleiadesData(path_data)
data_obs_Pleiades = pleiades_data.data
data_obs_Pleiades
| source_id | Mecayotl | Olivares+2018 | Meingast+2021 | l | b | ra | ra_error | dec | dec_error | ... | r_abs | i_abs | y_abs | z_abs | g_abs | G-J | G-RP | BP-RP | Lsun | log(L/Lsun) | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 66787119410915072 | True | True | True | 166.210733 | -23.276099 | 56.662143 | 0.014997 | 24.520108 | 0.009073 | ... | 7.418857 | 6.972857 | 6.525157 | 6.694857 | 8.224757 | 1.826829 | 0.810348 | 1.512766 | 0.155611 | -0.807959 |
| 1 | 64977705525131904 | True | True | True | 167.014088 | -24.105530 | 56.647305 | 0.018675 | 23.411531 | 0.012227 | ... | 8.549097 | 7.790097 | 7.159997 | 7.424497 | 9.772697 | 2.421177 | 1.006676 | 2.033531 | 0.076629 | -1.115607 |
| 2 | 65195404530870144 | True | True | True | 166.401835 | -23.959280 | 56.302660 | 0.187139 | 23.895691 | 0.137836 | ... | 14.008637 | 12.001337 | 10.590737 | 11.072637 | 15.369937 | 3.461519 | 1.369004 | 3.289309 | NaN | NaN |
| 3 | 64942001460286080 | True | True | True | 167.530351 | -23.713150 | 57.315181 | 0.117257 | 23.380170 | 0.079404 | ... | 13.164333 | 11.330633 | 10.050533 | 10.483733 | 14.439433 | 3.319764 | 1.337366 | 3.338682 | 0.007924 | -2.101050 |
| 4 | 64433924008996224 | True | True | True | 167.187075 | -25.469587 | 55.777180 | 0.158969 | 22.296886 | 0.108396 | ... | 13.654355 | 11.738355 | 10.399955 | 10.850355 | 15.065555 | 3.404532 | 1.361330 | 3.481064 | 0.006176 | -2.209322 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 941 | 68045583484082048 | True | True | True | 164.243788 | -25.259933 | 53.747213 | 0.023608 | 24.204450 | 0.018528 | ... | 9.355493 | 8.326793 | 7.636193 | 7.872993 | 10.560393 | 2.526645 | 1.083299 | 2.277171 | 0.052494 | -1.279886 |
| 942 | 65113559634339200 | True | True | True | 165.803768 | -25.252330 | 54.921665 | 0.014164 | 23.290687 | 0.010935 | ... | 4.161775 | 4.078775 | 4.069775 | 4.064775 | 4.429775 | 0.890571 | 0.437847 | 0.711143 | 1.683682 | 0.226260 |
| 943 | 64987876007763968 | True | True | True | 167.304764 | -23.830079 | 57.063558 | 0.061383 | 23.434686 | 0.049673 | ... | 11.529959 | 10.045959 | 9.017459 | 9.351059 | 12.720659 | 3.020585 | 1.246986 | 2.865516 | 0.015930 | -1.797787 |
| 944 | 71056252479453056 | True | True | True | 163.033014 | -22.724805 | 54.616063 | 0.095418 | 26.856932 | 0.065394 | ... | 12.175861 | 10.479061 | 9.307561 | 9.688561 | 13.449461 | 3.192930 | 1.292702 | 3.159868 | 0.011978 | -1.921621 |
| 945 | 65129506848731136 | True | True | True | 166.221501 | -24.634561 | 55.676696 | 0.013654 | 23.502637 | 0.009224 | ... | 6.892914 | 6.468914 | 6.072914 | 6.199914 | 7.756914 | 1.825894 | 0.746666 | 1.346799 | 0.238297 | -0.622882 |
946 rows × 100 columns
data_obs_Pleiades.columns
Index(['source_id', 'Mecayotl', 'Olivares+2018', 'Meingast+2021', 'l', 'b',
'ra', 'ra_error', 'dec', 'dec_error', 'parallax', 'parallax_error',
'pmra', 'pmra_error', 'pmdec', 'pmdec_error', 'pmra_pmdec_corr',
'ra_dec_corr', 'ra_parallax_corr', 'ra_pmra_corr', 'ra_pmdec_corr',
'dec_parallax_corr', 'dec_pmra_corr', 'dec_pmdec_corr',
'parallax_pmra_corr', 'parallax_pmdec_corr', 'g', 'bp', 'rp', 'e_g',
'e_bp', 'e_rp', 'dr3_radial_velocity', 'dr3_radial_velocity_error',
'ruwe', 'astrometric_excess_noise', 'astrometric_params_solved',
'bp_rp', 'g_rp', 'Jmag', 'Hmag', 'Kmag', 'e_Jmag', 'e_Hmag', 'e_Kmag',
'gmag', 'e_gmag', 'rmag', 'e_rmag', 'imag', 'e_imag', 'zmag', 'e_zmag',
'ymag', 'e_ymag', 'Name', 'EPIC', 'RAJ2000', 'DEJ2000', 'Vmag', 'J-K',
'Per', 'Amp', 'l_WLi', 'WLi', 'e_WLi', 'Teff', 'ALi', 'e_ALi', 'Bin',
'SimbadName', 'Teff_x', 'logg', '[Fe/H]', 'A0', 'AG', 'ABP', 'ARP',
'E(BP-RP)', 'Rad', 'Lib', 'angDist', 'distance', 'distance_modulus',
'G_abs', 'BP_abs', 'RP_abs', 'J_abs', 'H_abs', 'K_abs', 'r_abs',
'i_abs', 'y_abs', 'z_abs', 'g_abs', 'G-J', 'G-RP', 'BP-RP', 'Lsun',
'log(L/Lsun)'],
dtype='object')
data_obs_Pleiades['e_G'] = data_obs_Pleiades['e_g']
data_obs_Pleiades['e_RP'] = data_obs_Pleiades['e_rp']
data_obs_Pleiades['e_BP'] = data_obs_Pleiades['e_bp']
data_obs_Pleiades['e_J'] = data_obs_Pleiades['e_Jmag']
data_obs_Pleiades['e_K'] = data_obs_Pleiades['e_Kmag']
data_obs_Pleiades['e_H'] = data_obs_Pleiades['e_Hmag']
data_obs_Pleiades['e_r'] = data_obs_Pleiades['e_rmag']
data_obs_Pleiades['e_i'] = data_obs_Pleiades['e_imag']
data_obs_Pleiades['e_z'] = data_obs_Pleiades['e_zmag']
data_obs_Pleiades['e_y'] = data_obs_Pleiades['e_ymag']
data_obs_Pleiades['e_gmag'] = data_obs_Pleiades['e_gmag']
data_obs_Pleiades['Teff_x']
0 4410.5390
1 3664.9885
2 NaN
3 3131.1300
4 3092.2827
...
941 3602.4023
942 6055.7964
943 3315.6130
944 3214.3660
945 4741.1820
Name: Teff_x, Length: 946, dtype: float64
np.mean(data_obs_Pleiades['ALi'])
2.227078431372549
#sigma = 5.67e-8 # W/m²/K⁴
solar_abundance = 1.05
e_solar_abundance = 0.10
Zsun = 0.01524
#Asplund et al. 2009
Models¶
from models_test import Models
models = Models()
from models_test import PleiadesData
path_data = path_all + 'data/Pleiades_DANCe+GDR3+2MASS+PanSTARRS1+A_Li+Lbol.csv'
pleiades_data = PleiadesData(path_data)
data_obs_Pleiades = pleiades_data.data
path_models = path_all + 'data/BT-Settl_all_Myr_Gaia+2MASS+PanSTARRS.csv'
BTSettl_mod = models.BTSettl(path_models)
BTSettl_Li_isochrones = BTSettl_mod.BTSettl_Li_isochrones
spots_instance = models.SPOTS(path_all)
SPOTS = spots_instance.SPOTS
spots_instance = models.SPOTS_YBC(path_all)
SPOTS_edr3 = spots_instance.SPOTS_edr3
from models_test import PlotAnalyzer
PARSEC¶
parsec = models.PARSEC(path_all)
PARSEC_00 = parsec.get_dataframe()
PARSEC_00
| Zini | MH | logAge | Mini | int_IMF | M/Ms | logL | logTe | logg | label | ... | wP1_i45 | wP1_i50 | wP1_i55 | wP1_i60 | wP1_i65 | wP1_i70 | wP1_i75 | wP1_i80 | wP1_i85 | wP1_i90 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.01471 | 0.0 | 6.30103 | 0.090000 | 1.081690 | 0.090 | -1.379 | 3.4384 | 3.477 | 0.0 | ... | 10.378 | 10.378 | 10.378 | 10.378 | 10.378 | 10.378 | 10.378 | 10.378 | 10.378 | 10.378 |
| 1 | 0.01471 | 0.0 | 6.30103 | 0.097813 | 1.152571 | 0.098 | -1.307 | 3.4443 | 3.465 | 0.0 | ... | 10.112 | 10.112 | 10.112 | 10.112 | 10.112 | 10.112 | 10.112 | 10.112 | 10.112 | 10.112 |
| 2 | 0.01471 | 0.0 | 6.30103 | 0.100000 | 1.171106 | 0.100 | -1.288 | 3.4459 | 3.462 | 0.0 | ... | 10.042 | 10.042 | 10.042 | 10.042 | 10.042 | 10.042 | 10.042 | 10.042 | 10.042 | 10.042 |
| 3 | 0.01471 | 0.0 | 6.30103 | 0.100258 | 1.173256 | 0.100 | -1.286 | 3.4461 | 3.461 | 0.0 | ... | 10.035 | 10.035 | 10.035 | 10.035 | 10.035 | 10.035 | 10.035 | 10.035 | 10.035 | 10.035 |
| 4 | 0.01471 | 0.0 | 6.30103 | 0.109296 | 1.244379 | 0.109 | -1.224 | 3.4508 | 3.455 | 0.0 | ... | 9.811 | 9.811 | 9.811 | 9.811 | 9.811 | 9.811 | 9.811 | 9.811 | 9.811 | 9.811 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 41620 | 0.01471 | 0.0 | 8.77597 | 2.692088 | 2.589498 | 2.690 | 2.284 | 3.6668 | 2.203 | 7.0 | ... | -0.810 | -0.810 | -0.810 | -0.810 | -0.810 | -0.810 | -0.810 | -0.810 | -0.810 | -0.810 |
| 41621 | 0.01471 | 0.0 | 8.77597 | 2.692170 | 2.589500 | 2.690 | 2.276 | 3.6678 | 2.215 | 7.0 | ... | -0.793 | -0.793 | -0.793 | -0.793 | -0.793 | -0.793 | -0.793 | -0.793 | -0.793 | -0.793 |
| 41622 | 0.01471 | 0.0 | 8.77597 | 2.692199 | 2.589501 | 2.690 | 2.250 | 3.6700 | 2.249 | 7.0 | ... | -0.737 | -0.737 | -0.737 | -0.737 | -0.737 | -0.737 | -0.737 | -0.737 | -0.737 | -0.737 |
| 41623 | 0.01471 | 0.0 | 8.77597 | 2.692242 | 2.589502 | 2.690 | 2.217 | 3.6727 | 2.294 | 7.0 | ... | -0.663 | -0.663 | -0.663 | -0.663 | -0.663 | -0.663 | -0.663 | -0.663 | -0.663 | -0.663 |
| 41624 | 0.01471 | 0.0 | 8.77597 | 2.692394 | 2.589505 | 2.690 | 2.213 | 3.6727 | 2.297 | 7.0 | ... | -0.653 | -0.653 | -0.653 | -0.653 | -0.653 | -0.653 | -0.653 | -0.653 | -0.653 | -0.653 |
41625 rows × 312 columns
PARSEC_00_sun = parsec.get_dataframe(sun=True)
PARSEC_00_sun
| Zini | MH | logAge | Mini | int_IMF | M/Ms | logL | logTe | logg | label | ... | wP1_i45 | wP1_i50 | wP1_i55 | wP1_i60 | wP1_i65 | wP1_i70 | wP1_i75 | wP1_i80 | wP1_i85 | wP1_i90 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.00713 | -0.3272 | 6.30103 | 0.090000 | 1.081690 | 0.090 | -1.270 | 3.4655 | 3.477 | 0.0 | ... | 9.549 | 9.549 | 9.549 | 9.549 | 9.549 | 9.549 | 9.549 | 9.549 | 9.549 | 9.549 |
| 1 | 0.00713 | -0.3272 | 6.30103 | 0.096622 | 1.142251 | 0.097 | -1.208 | 3.4700 | 3.463 | 0.0 | ... | 9.346 | 9.346 | 9.346 | 9.346 | 9.346 | 9.346 | 9.346 | 9.346 | 9.346 | 9.346 |
| 2 | 0.00713 | -0.3272 | 6.30103 | 0.099697 | 1.168569 | 0.100 | -1.182 | 3.4720 | 3.458 | 0.0 | ... | 9.261 | 9.261 | 9.261 | 9.261 | 9.261 | 9.261 | 9.261 | 9.261 | 9.261 | 9.261 |
| 3 | 0.00713 | -0.3272 | 6.30103 | 0.108216 | 1.236287 | 0.108 | -1.115 | 3.4768 | 3.446 | 0.0 | ... | 9.047 | 9.047 | 9.047 | 9.047 | 9.047 | 9.047 | 9.047 | 9.047 | 9.047 | 9.047 |
| 4 | 0.00713 | -0.3272 | 6.30103 | 0.113255 | 1.273161 | 0.113 | -1.081 | 3.4794 | 3.442 | 0.0 | ... | 8.936 | 8.936 | 8.936 | 8.936 | 8.936 | 8.936 | 8.936 | 8.936 | 8.936 | 8.936 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 37339 | 0.00713 | -0.3272 | 8.77597 | 2.546733 | 2.586173 | 2.546 | 2.300 | 3.6824 | 2.226 | 7.0 | ... | -0.889 | -0.889 | -0.889 | -0.889 | -0.889 | -0.889 | -0.889 | -0.889 | -0.889 | -0.889 |
| 37340 | 0.00713 | -0.3272 | 8.77597 | 2.546813 | 2.586175 | 2.546 | 2.297 | 3.6829 | 2.230 | 7.0 | ... | -0.886 | -0.886 | -0.886 | -0.886 | -0.886 | -0.886 | -0.886 | -0.886 | -0.886 | -0.886 |
| 37341 | 0.00713 | -0.3272 | 8.77597 | 2.546835 | 2.586176 | 2.546 | 2.279 | 3.6845 | 2.254 | 7.0 | ... | -0.845 | -0.845 | -0.845 | -0.845 | -0.845 | -0.845 | -0.845 | -0.845 | -0.845 | -0.845 |
| 37342 | 0.00713 | -0.3272 | 8.77597 | 2.546864 | 2.586176 | 2.546 | 2.255 | 3.6866 | 2.287 | 7.0 | ... | -0.789 | -0.789 | -0.789 | -0.789 | -0.789 | -0.789 | -0.789 | -0.789 | -0.789 | -0.789 |
| 37343 | 0.00713 | -0.3272 | 8.77597 | 2.547009 | 2.586180 | 2.546 | 2.235 | 3.6877 | 2.312 | 7.0 | ... | -0.743 | -0.743 | -0.743 | -0.743 | -0.743 | -0.743 | -0.743 | -0.743 | -0.743 | -0.743 |
37344 rows × 312 columns
age = 0.120
PARSEC_00_120 = parsec.get_dataframe_by_age(age)
PARSEC_00_120
| Zini | MH | logAge | Mini | int_IMF | M/Ms | logL | logTe | logg | label | ... | wP1_i55 | wP1_i60 | wP1_i65 | wP1_i70 | wP1_i75 | wP1_i80 | wP1_i85 | wP1_i90 | Teff | Lsun | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.01471 | 0.0 | 8.08636 | 0.090000 | 1.081690 | 0.090 | -2.818 | 3.4010 | 4.766 | 0.0 | ... | 14.470 | 14.470 | 14.470 | 14.470 | 14.470 | 14.470 | 14.470 | 14.470 | 2517.676928 | 0.001521 |
| 1 | 0.01471 | 0.0 | 8.08636 | 0.091255 | 1.093600 | 0.091 | -2.808 | 3.4024 | 4.768 | 0.0 | ... | 14.445 | 14.445 | 14.445 | 14.445 | 14.445 | 14.445 | 14.445 | 14.445 | 2525.806055 | 0.001556 |
| 2 | 0.01471 | 0.0 | 8.08636 | 0.098871 | 1.161602 | 0.099 | -2.744 | 3.4117 | 4.776 | 0.0 | ... | 14.267 | 14.267 | 14.267 | 14.267 | 14.267 | 14.267 | 14.267 | 14.267 | 2580.477044 | 0.001803 |
| 3 | 0.01471 | 0.0 | 8.08636 | 0.100000 | 1.171106 | 0.100 | -2.735 | 3.4131 | 4.777 | 0.0 | ... | 14.229 | 14.229 | 14.229 | 14.229 | 14.229 | 14.229 | 14.229 | 14.229 | 2588.808942 | 0.001841 |
| 4 | 0.01471 | 0.0 | 8.08636 | 0.100865 | 1.178290 | 0.101 | -2.729 | 3.4140 | 4.778 | 0.0 | ... | 14.205 | 14.205 | 14.205 | 14.205 | 14.205 | 14.205 | 14.205 | 14.205 | 2594.179362 | 0.001866 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 120 | 0.01471 | 0.0 | 8.08636 | 2.699229 | 2.589651 | 2.699 | 1.794 | 4.0560 | 4.252 | 1.0 | ... | 0.913 | 0.913 | 0.913 | 0.913 | 0.913 | 0.913 | 0.913 | 0.913 | 11376.272858 | 62.230029 |
| 121 | 0.01471 | 0.0 | 8.08636 | 2.800000 | 2.591712 | 2.800 | 1.862 | 4.0656 | 4.238 | 1.0 | ... | 0.792 | 0.792 | 0.792 | 0.792 | 0.792 | 0.792 | 0.792 | 0.792 | 11630.543233 | 72.777980 |
| 122 | 0.01471 | 0.0 | 8.08636 | 3.000000 | 2.595334 | 3.000 | 1.992 | 4.0829 | 4.207 | 1.0 | ... | 0.560 | 0.560 | 0.560 | 0.560 | 0.560 | 0.560 | 0.560 | 0.560 | 12103.194151 | 98.174794 |
| 123 | 0.01471 | 0.0 | 8.08636 | 3.181551 | 2.598173 | 3.181 | 2.105 | 4.0971 | 4.176 | 1.0 | ... | 0.356 | 0.356 | 0.356 | 0.356 | 0.356 | 0.356 | 0.356 | 0.356 | 12505.469461 | 127.350308 |
| 124 | 0.01471 | 0.0 | 8.08636 | 3.200000 | 2.598441 | 3.199 | 2.116 | 4.0984 | 4.172 | 1.0 | ... | 0.334 | 0.334 | 0.334 | 0.334 | 0.334 | 0.334 | 0.334 | 0.334 | 12542.958923 | 130.617089 |
125 rows × 314 columns
PARSEC_00_120['Y']
0 0.2745
1 0.2745
2 0.2745
3 0.2745
4 0.2745
...
120 0.2746
121 0.2746
122 0.2746
123 0.2746
124 0.2746
Name: Y, Length: 125, dtype: float64
PARSEC_00_sun_120 = parsec.get_dataframe_by_age(age, sun=True)
PARSEC_00_sun_120
| Zini | MH | logAge | Mini | int_IMF | M/Ms | logL | logTe | logg | label | ... | wP1_i55 | wP1_i60 | wP1_i65 | wP1_i70 | wP1_i75 | wP1_i80 | wP1_i85 | wP1_i90 | Teff | Lsun | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.00713 | -0.3272 | 8.08636 | 0.090000 | 1.081690 | 0.090 | -2.769 | 3.4339 | 4.849 | 0.0 | ... | 13.741 | 13.741 | 13.741 | 13.741 | 13.741 | 13.741 | 13.741 | 13.741 | 2715.813858 | 0.001702 |
| 1 | 0.00713 | -0.3272 | 8.08636 | 0.090711 | 1.088470 | 0.091 | -2.764 | 3.4346 | 4.850 | 0.0 | ... | 13.717 | 13.717 | 13.717 | 13.717 | 13.717 | 13.717 | 13.717 | 13.717 | 2720.194762 | 0.001722 |
| 2 | 0.00713 | -0.3272 | 8.08636 | 0.096212 | 1.138659 | 0.096 | -2.721 | 3.4400 | 4.855 | 0.0 | ... | 13.526 | 13.526 | 13.526 | 13.526 | 13.526 | 13.526 | 13.526 | 13.526 | 2754.228703 | 0.001901 |
| 3 | 0.00713 | -0.3272 | 8.08636 | 0.099697 | 1.168569 | 0.100 | -2.694 | 3.4436 | 4.857 | 0.0 | ... | 13.404 | 13.404 | 13.404 | 13.404 | 13.404 | 13.404 | 13.404 | 13.404 | 2777.154236 | 0.002023 |
| 4 | 0.00713 | -0.3272 | 8.08636 | 0.105658 | 1.216707 | 0.106 | -2.653 | 3.4488 | 4.862 | 0.0 | ... | 13.224 | 13.224 | 13.224 | 13.224 | 13.224 | 13.224 | 13.224 | 13.224 | 2810.606200 | 0.002223 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 160 | 0.00713 | -0.3272 | 8.08636 | 3.026102 | 2.595767 | 3.025 | 2.093 | 4.1248 | 4.277 | 1.0 | ... | 0.568 | 0.568 | 0.568 | 0.568 | 0.568 | 0.568 | 0.568 | 0.568 | 13329.074642 | 123.879659 |
| 161 | 0.00713 | -0.3272 | 8.08636 | 3.171499 | 2.598026 | 3.170 | 2.183 | 4.1354 | 4.250 | 1.0 | ... | 0.405 | 0.405 | 0.405 | 0.405 | 0.405 | 0.405 | 0.405 | 0.405 | 13658.405431 | 152.405275 |
| 162 | 0.00713 | -0.3272 | 8.08636 | 3.181142 | 2.598167 | 3.180 | 2.189 | 4.1361 | 4.248 | 1.0 | ... | 0.394 | 0.394 | 0.394 | 0.394 | 0.394 | 0.394 | 0.394 | 0.394 | 13680.437931 | 154.525444 |
| 163 | 0.00713 | -0.3272 | 8.08636 | 3.362140 | 2.600650 | 3.360 | 2.297 | 4.1477 | 4.210 | 1.0 | ... | 0.191 | 0.191 | 0.191 | 0.191 | 0.191 | 0.191 | 0.191 | 0.191 | 14050.765963 | 198.152703 |
| 164 | 0.00713 | -0.3272 | 8.08636 | 3.368611 | 2.600733 | 3.366 | 2.301 | 4.1481 | 4.209 | 1.0 | ... | 0.184 | 0.184 | 0.184 | 0.184 | 0.184 | 0.184 | 0.184 | 0.184 | 14063.713158 | 199.986187 |
165 rows × 314 columns
PARSEC_00_sun_120['Y']
0 0.2615
1 0.2615
2 0.2615
3 0.2615
4 0.2615
...
160 0.2614
161 0.2614
162 0.2614
163 0.2614
164 0.2614
Name: Y, Length: 165, dtype: float64
PARSEC_iso_omega_00_Phot_dict, PARSEC_iso_omega_00_sun_Phot_dict = parsec._generate_dicts()
BT-Settl¶
path_models = path_all + 'data/BT-Settl_all_Myr_Gaia+2MASS+PanSTARRS.csv'
BTSettl_mod = models.BTSettl(path_models)
BTSettl = BTSettl_mod.get_dataframe()
BTSettl
| age_Gyr | t(Gyr) | M/Ms | Teff | log(L/Lsun) | lg(g) | R(Gcm) | D | Li | G_abs | ... | J_abs | H_abs | K_abs | g_abs | r_abs | i_abs | y_abs | z_abs | A(Li) | Lsun | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.001 | 0.001 | 0.010 | 2345.0 | -2.70 | 3.57 | 18.99 | 1.00 | 1.0000 | 14.055 | ... | 9.328 | 8.770 | 8.353 | 17.937 | 17.095 | 13.757 | 12.125 | 11.009 | 3.300000 | 0.001995 |
| 1 | 0.001 | 0.001 | 0.015 | 2504.0 | -2.42 | 3.58 | 22.90 | 1.00 | 1.0000 | 13.015 | ... | 8.667 | 8.135 | 7.751 | 16.291 | 15.787 | 12.540 | 11.155 | 10.214 | 3.300000 | 0.003802 |
| 2 | 0.001 | 0.001 | 0.020 | 2598.0 | -2.25 | 3.59 | 26.11 | 1.00 | 1.0000 | 12.381 | ... | 8.238 | 7.710 | 7.347 | 15.366 | 14.935 | 11.827 | 10.566 | 9.722 | 3.300000 | 0.005623 |
| 3 | 0.001 | 0.001 | 0.030 | 2710.0 | -1.98 | 3.57 | 32.78 | 1.00 | 1.0000 | 11.507 | ... | 7.582 | 7.054 | 6.720 | 14.194 | 13.747 | 10.885 | 9.754 | 9.006 | 3.300000 | 0.010471 |
| 4 | 0.001 | 0.001 | 0.040 | 2779.0 | -1.81 | 3.57 | 37.62 | 0.99 | 1.0000 | 10.990 | ... | 7.188 | 6.652 | 6.338 | 13.512 | 13.061 | 10.339 | 9.275 | 8.580 | 3.300000 | 0.015488 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 833 | 10.000 | 10.000 | 0.500 | 3689.0 | -1.43 | 4.78 | 33.05 | 0.00 | 0.0000 | 8.937 | ... | 6.589 | 5.944 | 5.721 | 10.162 | 9.297 | 8.342 | 7.936 | 7.713 | -inf | 0.037154 |
| 834 | 10.000 | 10.000 | 0.600 | 4013.0 | -1.12 | 4.70 | 39.92 | 0.00 | 0.0000 | 7.960 | ... | 5.926 | 5.237 | 5.059 | 9.010 | 8.100 | 7.463 | 7.188 | 7.015 | -inf | 0.075858 |
| 835 | 10.000 | 10.000 | 0.700 | 4493.0 | -0.79 | 4.63 | 46.43 | 0.00 | 0.0000 | 6.900 | ... | 5.244 | 4.614 | 4.490 | 7.754 | 6.870 | 6.537 | 6.392 | 6.271 | -inf | 0.162181 |
| 836 | 10.000 | 10.000 | 0.800 | 5002.0 | -0.47 | 4.55 | 54.44 | 0.00 | 0.0000 | 5.925 | ... | 4.580 | 4.098 | 4.000 | 6.529 | 5.885 | 5.675 | 5.594 | 5.525 | -inf | 0.338844 |
| 837 | 10.000 | 10.000 | 0.900 | 5495.0 | -0.12 | 4.42 | 67.60 | 0.00 | 0.0626 | 4.972 | ... | 3.860 | 3.490 | 3.410 | 5.422 | 4.944 | 4.804 | 4.769 | 4.741 | 2.096574 | 0.758578 |
838 rows × 22 columns
age = 0.120
BTSettl_120 = BTSettl_mod.get_dataframe_by_age(age)
BTSettl_120
| age_Gyr | t(Gyr) | M/Ms | Teff | log(L/Lsun) | lg(g) | R(Gcm) | D | Li | G_abs | ... | J_abs | H_abs | K_abs | g_abs | r_abs | i_abs | y_abs | z_abs | A(Li) | Lsun | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 488 | 0.12 | 0.12 | 0.020 | 1304.0 | -4.41 | 4.56 | 8.58 | 0.0 | 1.0000 | 19.367000 | ... | 14.167 | 12.877 | 12.291 | 25.102 | 22.184 | 20.307 | 17.194 | 16.000 | 3.300000 | 0.000039 |
| 489 | 0.12 | 0.12 | 0.030 | 1779.0 | -3.88 | 4.75 | 8.44 | 0.0 | 1.0000 | 18.128999 | ... | 12.882 | 11.710 | 10.977 | 22.719 | 19.781 | 17.869 | 16.276 | 15.153 | 3.300000 | 0.000132 |
| 490 | 0.12 | 0.12 | 0.040 | 2225.0 | -3.46 | 4.84 | 8.79 | 0.0 | 1.0000 | 16.322999 | ... | 11.268 | 10.580 | 10.210 | 21.486 | 18.909 | 16.300 | 14.352 | 13.157 | 3.300000 | 0.000347 |
| 491 | 0.12 | 0.12 | 0.050 | 2471.0 | -3.23 | 4.88 | 9.31 | 0.0 | 0.9970 | 15.099000 | ... | 10.655 | 10.068 | 9.746 | 19.253 | 17.727 | 14.750 | 13.173 | 12.261 | 3.298695 | 0.000589 |
| 492 | 0.12 | 0.12 | 0.060 | 2635.0 | -3.06 | 4.91 | 9.89 | 0.0 | 0.9010 | 14.358000 | ... | 10.289 | 9.710 | 9.397 | 17.811 | 16.792 | 13.812 | 12.518 | 11.764 | 3.254725 | 0.000871 |
| 493 | 0.12 | 0.12 | 0.070 | 2757.0 | -2.94 | 4.93 | 10.47 | 0.0 | 0.2440 | 13.784000 | ... | 10.011 | 9.431 | 9.125 | 16.576 | 15.826 | 13.134 | 12.043 | 11.410 | 2.687390 | 0.001148 |
| 494 | 0.12 | 0.12 | 0.072 | 2780.0 | -2.91 | 4.93 | 10.59 | 0.0 | 0.1110 | 13.674000 | ... | 9.959 | 9.379 | 9.073 | 16.387 | 15.630 | 13.008 | 11.952 | 11.340 | 2.345323 | 0.001230 |
| 495 | 0.12 | 0.12 | 0.075 | 2810.0 | -2.88 | 4.93 | 10.78 | 0.0 | 0.0171 | 13.533000 | ... | 9.884 | 9.304 | 9.001 | 16.173 | 15.393 | 12.851 | 11.833 | 11.247 | 1.532996 | 0.001318 |
| 496 | 0.12 | 0.12 | 0.080 | 2853.0 | -2.83 | 4.94 | 11.08 | 0.0 | 0.0001 | 13.363000 | ... | 9.771 | 9.192 | 8.896 | 15.990 | 15.143 | 12.668 | 11.679 | 11.119 | -0.700000 | 0.001479 |
| 497 | 0.12 | 0.12 | 0.090 | 2924.0 | -2.74 | 4.94 | 11.69 | 0.0 | 0.0000 | 13.058000 | ... | 9.569 | 8.992 | 8.707 | 15.614 | 14.704 | 12.346 | 11.408 | 10.891 | -inf | 0.001820 |
| 498 | 0.12 | 0.12 | 0.100 | 2978.0 | -2.67 | 4.95 | 12.25 | 0.0 | 0.0000 | 12.800000 | ... | 9.401 | 8.824 | 8.547 | 15.218 | 14.336 | 12.076 | 11.184 | 10.700 | -inf | 0.002138 |
| 499 | 0.12 | 0.12 | 0.200 | 3270.0 | -2.19 | 4.93 | 17.62 | 0.0 | 0.0000 | 11.203000 | ... | 8.332 | 7.736 | 7.483 | 12.818 | 12.050 | 10.505 | 9.871 | 9.538 | -inf | 0.006457 |
| 500 | 0.12 | 0.12 | 0.300 | 3411.0 | -1.91 | 4.90 | 22.28 | 0.0 | 0.0000 | 10.378000 | ... | 7.696 | 7.088 | 6.844 | 11.838 | 11.027 | 9.708 | 9.158 | 8.868 | -inf | 0.012303 |
| 501 | 0.12 | 0.12 | 0.400 | 3536.0 | -1.68 | 4.86 | 26.97 | 0.0 | 0.0000 | 9.696000 | ... | 7.167 | 6.546 | 6.310 | 11.042 | 10.207 | 9.056 | 8.573 | 8.315 | -inf | 0.020893 |
| 502 | 0.12 | 0.12 | 0.500 | 3727.0 | -1.42 | 4.78 | 33.02 | 0.0 | 0.0000 | 8.867000 | ... | 6.562 | 5.912 | 5.693 | 10.069 | 9.195 | 8.284 | 7.896 | 7.679 | -inf | 0.038019 |
| 503 | 0.12 | 0.12 | 0.600 | 4030.0 | -1.15 | 4.73 | 38.33 | 0.0 | 0.0000 | 8.019000 | ... | 6.001 | 5.316 | 5.139 | 9.077 | 8.145 | 7.524 | 7.255 | 7.085 | -inf | 0.070795 |
| 504 | 0.12 | 0.12 | 0.700 | 4400.0 | -0.88 | 4.68 | 43.93 | 0.0 | 0.0000 | 7.152000 | ... | 5.427 | 4.774 | 4.642 | 8.061 | 7.135 | 6.762 | 6.598 | 6.469 | -inf | 0.131826 |
| 505 | 0.12 | 0.12 | 0.800 | 4825.0 | -0.61 | 4.63 | 49.66 | 0.0 | 0.0046 | 6.326000 | ... | 4.883 | 4.352 | 4.246 | 7.006 | 6.284 | 6.040 | 5.940 | 5.854 | 0.962758 | 0.245471 |
| 506 | 0.12 | 0.12 | 0.900 | 5208.0 | -0.37 | 4.58 | 56.22 | 0.0 | 0.0822 | 5.641000 | ... | 4.399 | 3.967 | 3.877 | 6.174 | 5.604 | 5.426 | 5.366 | 5.315 | 2.214872 | 0.426580 |
| 507 | 0.12 | 0.12 | 1.000 | 5547.0 | -0.16 | 4.52 | 63.41 | 0.0 | 0.3070 | 5.065000 | ... | 3.974 | 3.614 | 3.536 | 5.503 | 5.038 | 4.904 | 4.873 | 4.848 | 2.787138 | 0.691831 |
| 508 | 0.12 | 0.12 | 1.100 | 5889.0 | 0.05 | 4.45 | 71.63 | 0.0 | 0.5740 | 4.514000 | ... | 3.560 | 3.264 | 3.195 | 4.875 | 4.498 | 4.403 | 4.398 | 4.396 | 3.058912 | 1.122018 |
| 509 | 0.12 | 0.12 | 1.200 | 6172.0 | 0.24 | 4.39 | 80.96 | 0.0 | 0.7700 | 4.038000 | ... | 3.187 | 2.936 | 2.872 | 4.341 | 4.032 | 3.967 | 3.983 | 3.996 | 3.186491 | 1.737801 |
| 510 | 0.12 | 0.12 | 1.300 | 6455.0 | 0.41 | 4.33 | 90.19 | 0.0 | 0.8800 | 3.605000 | ... | 2.853 | 2.643 | 2.583 | 3.855 | 3.609 | 3.574 | 3.610 | 3.634 | 3.244483 | 2.570396 |
| 511 | 0.12 | 0.12 | 1.400 | 6767.0 | 0.56 | 4.29 | 97.82 | 0.0 | 0.9380 | 3.226000 | ... | 2.576 | 2.408 | 2.352 | 3.422 | 3.242 | 3.240 | 3.297 | 3.331 | 3.272203 | 3.630781 |
24 rows × 22 columns
min(BTSettl_120['Teff'])
1304.0
max(BTSettl['Teff'])
6768.0
BTSettl_Li_isochrones = BTSettl_mod.BTSettl_Li_isochrones
BTSettl_Li_isochrones.keys()
dict_keys([0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009000000000000001, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.12, 0.15, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7000000000000001, 0.8, 0.9, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0])
len(BTSettl_Li_isochrones.keys())
39
BTSettl_Li_isochrones = {round(key, 3): value for key, value in BTSettl_Li_isochrones.items()}
BTSettl_Li_isochrones.keys()
dict_keys([0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.12, 0.15, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0])
len(BTSettl_Li_isochrones.keys())
39
BTSettl_Li_isochrones_Teff = BTSettl_Li_isochrones[0.120][BTSettl_Li_isochrones[0.120]['Teff'] < 2955]
BTSettl_Li_isochrones_Teff = BTSettl_Li_isochrones_Teff[BTSettl_Li_isochrones_Teff ['Teff'] > 1600]
BTSettl_Li_isochrones_Teff
| age_Gyr | t(Gyr) | M/Ms | Teff | log(L/Lsun) | lg(g) | R(Gcm) | D | Li | G_abs | ... | J_abs | H_abs | K_abs | g_abs | r_abs | i_abs | y_abs | z_abs | A(Li) | Lsun | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 489 | 0.12 | 0.12 | 0.030 | 1779.0 | -3.88 | 4.75 | 8.44 | 0.0 | 1.0000 | 18.128999 | ... | 12.882 | 11.710 | 10.977 | 22.719 | 19.781 | 17.869 | 16.276 | 15.153 | 3.300000 | 0.000132 |
| 490 | 0.12 | 0.12 | 0.040 | 2225.0 | -3.46 | 4.84 | 8.79 | 0.0 | 1.0000 | 16.322999 | ... | 11.268 | 10.580 | 10.210 | 21.486 | 18.909 | 16.300 | 14.352 | 13.157 | 3.300000 | 0.000347 |
| 491 | 0.12 | 0.12 | 0.050 | 2471.0 | -3.23 | 4.88 | 9.31 | 0.0 | 0.9970 | 15.099000 | ... | 10.655 | 10.068 | 9.746 | 19.253 | 17.727 | 14.750 | 13.173 | 12.261 | 3.298695 | 0.000589 |
| 492 | 0.12 | 0.12 | 0.060 | 2635.0 | -3.06 | 4.91 | 9.89 | 0.0 | 0.9010 | 14.358000 | ... | 10.289 | 9.710 | 9.397 | 17.811 | 16.792 | 13.812 | 12.518 | 11.764 | 3.254725 | 0.000871 |
| 493 | 0.12 | 0.12 | 0.070 | 2757.0 | -2.94 | 4.93 | 10.47 | 0.0 | 0.2440 | 13.784000 | ... | 10.011 | 9.431 | 9.125 | 16.576 | 15.826 | 13.134 | 12.043 | 11.410 | 2.687390 | 0.001148 |
| 494 | 0.12 | 0.12 | 0.072 | 2780.0 | -2.91 | 4.93 | 10.59 | 0.0 | 0.1110 | 13.674000 | ... | 9.959 | 9.379 | 9.073 | 16.387 | 15.630 | 13.008 | 11.952 | 11.340 | 2.345323 | 0.001230 |
| 495 | 0.12 | 0.12 | 0.075 | 2810.0 | -2.88 | 4.93 | 10.78 | 0.0 | 0.0171 | 13.533000 | ... | 9.884 | 9.304 | 9.001 | 16.173 | 15.393 | 12.851 | 11.833 | 11.247 | 1.532996 | 0.001318 |
| 496 | 0.12 | 0.12 | 0.080 | 2853.0 | -2.83 | 4.94 | 11.08 | 0.0 | 0.0001 | 13.363000 | ... | 9.771 | 9.192 | 8.896 | 15.990 | 15.143 | 12.668 | 11.679 | 11.119 | -0.700000 | 0.001479 |
| 497 | 0.12 | 0.12 | 0.090 | 2924.0 | -2.74 | 4.94 | 11.69 | 0.0 | 0.0000 | 13.058000 | ... | 9.569 | 8.992 | 8.707 | 15.614 | 14.704 | 12.346 | 11.408 | 10.891 | -inf | 0.001820 |
9 rows × 22 columns
BTSettl_Li_isochrones_Teff['BP_abs']-BTSettl_Li_isochrones_Teff['RP_abs']
489 4.853999 490 5.589999 491 5.210000 492 4.732000 493 4.146000 494 4.050000 495 3.950000 496 3.889000 497 3.759000 dtype: float64
MIST¶
mist_instance = models.MIST(path_all)
phot = 'UBVRIplus'
feh = 'm0.25'
vvcrit = 0.0
mist_instance.file_copy(phot, vvcrit, feh)
phot_G2 = 'UBVRIplus'
phot_P = 'PanSTARRS'
feh = 'p0.00'
vvcrit = 0.0
MIST_FULL = mist_instance.read_iso(phot_G2, phot_P, vvcrit, feh)
phot_G2 = 'UBVRIplus'
phot_P = 'PanSTARRS'
feh = 'p0.25'
vvcrit = 0.0
MIST_FULL_2 = mist_instance.read_iso(phot_G2, phot_P, vvcrit, feh)
desired_age = 0.120
nearest_age = select_nearest_age(MIST_FULL, desired_age)
MIST_FULL[nearest_age]
Reading in: /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/data/MIST_v1.2_feh_p0.00_afe_p0.0_vvcrit0.0_PanSTARRS.iso.cmd
Reading in: /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/data/MIST_v1.2_feh_p0.00_afe_p0.0_vvcrit0.0_UBVRIplus.iso.cmd
Reading in: /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/data/MIST_v1.2_vvcrit0.0_full_isos/MIST_v1.2_feh_p0.00_afe_p0.0_vvcrit0.0_full.iso
version: {'MIST': '1.2', 'MESA': '7503'}
abundances: {'Yinit': 0.2703, 'Zinit': 0.0142857, '[Fe/H]': 0.0, '[a/Fe]': 0.0}
rotation: 0.0
Reading in: /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/data/MIST_v1.2_feh_p0.25_afe_p0.0_vvcrit0.0_PanSTARRS.iso.cmd
Reading in: /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/data/MIST_v1.2_feh_p0.25_afe_p0.0_vvcrit0.0_UBVRIplus.iso.cmd
Reading in: /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/data/MIST_v1.2_vvcrit0.0_full_isos/MIST_v1.2_feh_p0.25_afe_p0.0_vvcrit0.0_full.iso
version: {'MIST': '1.2', 'MESA': '7503'}
abundances: {'Yinit': 0.2869, 'Zinit': 0.0254039, '[Fe/H]': 0.25, '[a/Fe]': 0.0}
rotation: 0.0
| EEP | log10_isochrone_age_yr | initial_mass | M/Ms | log_Teff | log_g | log_L | [Fe/H]_init | [Fe/H] | g | ... | Gaia_BP_MAWf | Gaia_RP_MAW | TESS | G | BP | RP | phase | surface_li7 | Teff | Lsun | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 153 | 8.1 | 0.100000 | 0.100000 | 3.472358 | 4.943898 | -2.661675 | 0.0 | 0.041367 | 15.182353 | ... | 14.818656 | 11.510085 | 11.390598 | 12.816993 | 14.800249 | 11.507612 | -1.0 | 4.509661e-20 | 2967.276575 | 0.002179 |
| 1 | 154 | 8.1 | 0.100487 | 0.100487 | 3.472502 | 4.943863 | -2.659350 | 0.0 | 0.041366 | 15.172418 | ... | 14.808554 | 11.503003 | 11.383737 | 12.809403 | 14.790139 | 11.500522 | -1.0 | 4.626529e-20 | 2968.258909 | 0.002191 |
| 2 | 155 | 8.1 | 0.104821 | 0.104821 | 3.473925 | 4.943235 | -2.637765 | 0.0 | 0.041366 | 15.077920 | ... | 14.712386 | 11.436582 | 11.319491 | 12.737943 | 14.693890 | 11.434015 | -1.0 | 5.808929e-20 | 2977.999206 | 0.002303 |
| 3 | 156 | 8.1 | 0.109198 | 0.109198 | 3.475536 | 4.942625 | -2.615297 | 0.0 | 0.041368 | 14.976478 | ... | 14.609145 | 11.366608 | 11.251957 | 12.662225 | 14.590545 | 11.363955 | -1.0 | 7.267820e-20 | 2989.068594 | 0.002425 |
| 4 | 157 | 8.1 | 0.113606 | 0.113606 | 3.477285 | 4.942037 | -2.592188 | 0.0 | 0.041368 | 14.870311 | ... | 14.501178 | 11.294242 | 11.182213 | 12.583573 | 14.482453 | 11.291506 | -1.0 | 9.055154e-20 | 3001.132776 | 0.002557 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 357 | 1706 | 8.1 | 4.873859 | 0.879580 | 4.653539 | 8.391392 | -0.440251 | 0.0 | 0.097281 | 9.765747 | ... | 9.720577 | 10.274443 | 10.278122 | 9.957335 | 9.783835 | 10.293779 | 6.0 | 4.627805e-19 | 45033.880846 | 0.362869 |
| 358 | 1707 | 8.1 | 4.875869 | 0.879720 | 4.645607 | 8.393120 | -0.473640 | 0.0 | 0.097241 | 9.789981 | ... | 9.745432 | 10.297119 | 10.300766 | 9.981316 | 9.808478 | 10.316435 | 6.0 | 4.579056e-19 | 44218.761646 | 0.336016 |
| 359 | 1708 | 8.1 | 4.878084 | 0.879874 | 4.637685 | 8.394857 | -0.506986 | 0.0 | 0.097196 | 9.814415 | ... | 9.770496 | 10.319976 | 10.323583 | 10.005491 | 9.833328 | 10.339262 | 6.0 | 4.529060e-19 | 43419.503570 | 0.311182 |
| 360 | 1709 | 8.1 | 4.880514 | 0.880044 | 4.629777 | 8.396619 | -0.540296 | 0.0 | 0.097147 | 9.839218 | ... | 9.795950 | 10.343163 | 10.346726 | 10.030030 | 9.858564 | 10.362416 | 6.0 | 4.457822e-19 | 42636.017717 | 0.288207 |
| 361 | 1710 | 8.1 | 4.883200 | 0.880231 | 4.621878 | 8.398361 | -0.573538 | 0.0 | 0.097092 | 9.864440 | ... | 9.821851 | 10.366711 | 10.370228 | 10.054979 | 9.884237 | 10.385928 | 6.0 | 4.363275e-19 | 41867.616468 | 0.266970 |
362 rows × 45 columns
[Fe/H] for Pleiades: +0.042 (Soderblom et al. 2009).
MIST_FULL[nearest_age].columns
Index(['EEP', 'log10_isochrone_age_yr', 'initial_mass', 'M/Ms', 'log_Teff',
'log_g', 'log_L', '[Fe/H]_init', '[Fe/H]', 'g', 'r', 'i', 'z', 'y', 'w',
'PS_open', 'phase', 'Bessell_U', 'Bessell_B', 'Bessell_V', 'Bessell_R',
'Bessell_I', 'J', 'H', 'K', 'Kepler_Kp', 'Kepler_D51', 'Hipparcos_Hp',
'Tycho_B', 'Tycho_V', 'Gaia_G_DR2Rev', 'Gaia_BP_DR2Rev',
'Gaia_RP_DR2Rev', 'Gaia_G_MAW', 'Gaia_BP_MAWb', 'Gaia_BP_MAWf',
'Gaia_RP_MAW', 'TESS', 'G', 'BP', 'RP', 'phase', 'surface_li7', 'Teff',
'Lsun'],
dtype='object')
plt.rcParams.update({'font.size': 14}) # Set the font size
fig, ax = plt.subplots(1, 1, figsize=(8, 6))
ax.set_ylabel('Mass Fraction Li')
ax.set_xlabel('$T_{eff}$ [K]')
ax.plot(10**MIST_FULL[nearest_age]['log_Teff'], MIST_FULL[nearest_age]['surface_li7'])
ax.set_xlim(2500, 10000)
ax.invert_xaxis()
max(10**MIST_FULL[nearest_age]['log_Teff'])
339646.6795258933
min(10**MIST_FULL[nearest_age]['log_Teff'])
2967.2765754069314
minTeff_array = []
for key in MIST_FULL.keys():
minTeff = min(10**MIST_FULL[key]['log_Teff'])
minTeff_array.append(minTeff)
min(minTeff_array)
2885.5722587919586
SPOTS¶
plt.rcParams.update({'font.size': 11, 'axes.linewidth': 1, 'axes.edgecolor': 'k'})
plt.rcParams['font.family'] = 'serif'
spots_instance = models.SPOTS(path_all)
SPOTS = spots_instance.SPOTS
SPOTS.keys()
dict_keys(['f017', 'f085', 'f051', 'f034', 'f068', 'f000'])
SPOTS['f000'].keys()
dict_keys([0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.011, 0.013, 0.014, 0.016, 0.018, 0.02, 0.022, 0.025, 0.028, 0.032, 0.035, 0.04, 0.045, 0.05, 0.056, 0.063, 0.071, 0.079, 0.089, 0.1, 0.112, 0.126, 0.141, 0.158, 0.178, 0.2, 0.224, 0.251, 0.282, 0.316, 0.355, 0.398, 0.447, 0.501, 0.562, 0.631, 0.708, 0.794, 0.891, 1.0, 1.122, 1.259, 1.413, 1.585, 1.778, 1.995, 2.239, 2.512, 2.818, 3.162, 3.548, 3.981])
SPOTS['f000'][0.126].columns
Index(['Mass', 'Fspot', 'Xspot', 'log(L/Lsun)', 'log(R/Rsun)', 'log(g)',
'log(Teff)', 'log(T_hot)', 'log(T_cool)', 'TauCZ', 'Li/Li0', 'B_mag',
'V_mag', 'Rc_mag', 'Ic_mag', 'J_mag', 'H_mag', 'K_mag', 'W1_mag',
'G_mag', 'BP_mag', 'RP_mag', 'Age [Gyr]', 'A(Li)', 'M/Ms', 'Teff',
'Lsun', 'G', 'BP', 'RP'],
dtype='object')
SPOTS['f085'][0.035]['BP_mag']-SPOTS['f085'][0.035]['RP_mag']
0 NaN 1 NaN 2 NaN 3 NaN 4 NaN 5 NaN 6 NaN 7 NaN 8 NaN 9 NaN 10 2.5453 11 2.4617 12 2.3605 13 2.1510 14 1.8895 15 1.6275 16 1.3964 17 1.2118 18 1.0748 19 0.9690 20 0.9308 21 0.8991 22 0.8394 23 0.7704 24 0.6691 dtype: float64
SPOTS['f085'][0.035]['Li/Li0']
0 0.99994 1 0.99501 2 0.91175 3 0.44079 4 0.01100 5 0.00002 6 0.00020 7 0.00404 8 0.03503 9 0.13395 10 0.29661 11 0.46932 12 0.61882 13 0.72710 14 0.79927 15 0.84324 16 0.87046 17 0.89569 18 0.91984 19 0.94082 20 0.95712 21 0.96947 22 0.97850 23 0.98504 24 0.98964 Name: Li/Li0, dtype: float64
fig, ax = plt.subplots(1, 1, figsize=(8, 6))
ax.set_ylabel('$Li/Li^0$')
ax.set_xlabel('$B_p-R_p$ [mag]')
ax.plot(SPOTS['f000'][0.025]['BP_mag']-SPOTS['f000'][0.025]['RP_mag'], SPOTS['f000'][0.025]['Li/Li0'], linewidth=1, label='SPOTS f000; 25 Myr')
ax.plot(SPOTS['f017'][0.006]['BP_mag']-SPOTS['f017'][0.006]['RP_mag'], SPOTS['f017'][0.006]['Li/Li0'], linewidth=1, label='SPOTS f017; 6 Myr')
ax.plot(SPOTS['f034'][0.010]['BP_mag']-SPOTS['f034'][0.010]['RP_mag'], SPOTS['f034'][0.010]['Li/Li0'], linewidth=1, label='SPOTS f034; 10 Myr')
ax.plot(SPOTS['f051'][0.016]['BP_mag']-SPOTS['f051'][0.016]['RP_mag'], SPOTS['f051'][0.016]['Li/Li0'], linewidth=1, label='SPOTS f051; 16 Myr')
ax.plot(SPOTS['f068'][0.028]['BP_mag']-SPOTS['f068'][0.028]['RP_mag'], SPOTS['f068'][0.028]['Li/Li0'], linewidth=1, label='SPOTS f068; 28 Myr')
ax.plot(SPOTS['f085'][0.035]['BP_mag']-SPOTS['f085'][0.035]['RP_mag'], SPOTS['f085'][0.035]['Li/Li0'], linewidth=1, label='SPOTS f085; 35 Myr')
ax.plot(SPOTS['f000'][0.126]['BP_mag']-SPOTS['f000'][0.126]['RP_mag'], SPOTS['f000'][0.126]['Li/Li0'], linewidth=1, linestyle='--', label='SPOTS f000; 126 Myr')
#ax.errorbar(data_obs_Pleiades['Teff'], data_obs_Pleiades['ALi'], yerr=data_obs_Pleiades['e_ALi'], fmt='.', zorder=0, color='r', elinewidth=1, capsize=2)
ax.legend(fontsize=12, loc='upper center', bbox_to_anchor=(1.25, 0.9))
<matplotlib.legend.Legend at 0x7f2476f4dcd0>
fig, ax = plt.subplots(1, 1, figsize=(8, 6))
ax.set_ylabel('$Li/Li^0$')
ax.set_xlabel('$G-R_p$ [mag]')
ax.plot(SPOTS['f000'][0.025]['G_mag']-SPOTS['f000'][0.025]['RP_mag'], np.log10(SPOTS['f000'][0.025]['Li/Li0'])+3.3, linewidth=1, label='SPOTS f000; 25 Myr')
ax.plot(SPOTS['f017'][0.006]['G_mag']-SPOTS['f017'][0.006]['RP_mag'], np.log10(SPOTS['f017'][0.006]['Li/Li0'])+3.3, linewidth=1, label='SPOTS f017; 6 Myr')
ax.plot(SPOTS['f034'][0.010]['G_mag']-SPOTS['f034'][0.010]['RP_mag'], np.log10(SPOTS['f034'][0.010]['Li/Li0'])+3.3, linewidth=1, label='SPOTS f034; 10 Myr')
ax.plot(SPOTS['f051'][0.016]['G_mag']-SPOTS['f051'][0.016]['RP_mag'], np.log10(SPOTS['f051'][0.016]['Li/Li0'])+3.3, linewidth=1, label='SPOTS f051; 16 Myr')
ax.plot(SPOTS['f068'][0.028]['G_mag']-SPOTS['f068'][0.028]['RP_mag'], np.log10(SPOTS['f068'][0.028]['Li/Li0'])+3.3, linewidth=1, label='SPOTS f068; 28 Myr')
ax.plot(SPOTS['f085'][0.035]['G_mag']-SPOTS['f085'][0.035]['RP_mag'], np.log10(SPOTS['f085'][0.035]['Li/Li0'])+3.3, linewidth=1, label='SPOTS f085; 35 Myr')
ax.plot(SPOTS['f000'][0.126]['G_mag']-SPOTS['f000'][0.126]['RP_mag'], np.log10(SPOTS['f000'][0.126]['Li/Li0'])+3.3, linewidth=1, linestyle='--', label='SPOTS f000; 126 Myr')
ax.errorbar(data_obs_Pleiades['G_abs']-data_obs_Pleiades['RP_abs'], data_obs_Pleiades['ALi'], yerr=data_obs_Pleiades['e_ALi'], fmt='.', zorder=0, color='r', elinewidth=1, capsize=2)
ax.legend(fontsize=12, loc='upper center', bbox_to_anchor=(1.25, 0.9))
/pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/biosc_env/lib64/python3.11/site-packages/pandas/core/arraylike.py:399: RuntimeWarning: divide by zero encountered in log10 result = getattr(ufunc, method)(*inputs, **kwargs)
<matplotlib.legend.Legend at 0x7f247eecea10>
fig, ax = plt.subplots(1, 1, figsize=(8, 6))
ax.set_ylabel('$G$ [mag]')
ax.set_xlabel('$B_p-R_p$ [mag]')
ax.plot(SPOTS['f000'][0.126]['BP_mag']-SPOTS['f000'][0.126]['RP_mag'], SPOTS['f000'][0.126]['G_mag'], linewidth=1, label='SPOTS f000; 126 Myr', color='k')
ax.plot(SPOTS['f051'][0.126]['BP_mag']-SPOTS['f051'][0.126]['RP_mag'], SPOTS['f051'][0.126]['G_mag'], linewidth=1, linestyle='--', label='SPOTS f051; 126 Myr', color='k')
ax.plot(SPOTS['f085'][0.126]['BP_mag']-SPOTS['f085'][0.126]['RP_mag'], SPOTS['f085'][0.126]['G_mag'], linewidth=1, linestyle=':', label='SPOTS f085; 126 Myr', color='k')
ax.errorbar(data_obs_Pleiades['BP_abs']-data_obs_Pleiades['RP_abs'], data_obs_Pleiades['G_abs'], yerr=data_obs_Pleiades['e_g'], fmt='.', zorder=0, color='r', elinewidth=1, capsize=2, alpha=0.125)
ax.legend(fontsize=12, loc='upper center', bbox_to_anchor=(1.25, 0.9))
ax.invert_yaxis()
fig, ax = plt.subplots(1, 1, figsize=(8, 6))
ax.set_ylabel('$G$ [mag]')
ax.set_xlabel('$G-J$ [mag]')
ax.plot(SPOTS['f000'][0.126]['G_mag']-SPOTS['f000'][0.126]['J_mag'], SPOTS['f000'][0.126]['G_mag'], linewidth=1, label='SPOTS f000; 126 Myr', color='k')
ax.plot(SPOTS['f051'][0.126]['G_mag']-SPOTS['f051'][0.126]['J_mag'], SPOTS['f051'][0.126]['G_mag'], linewidth=1, linestyle='--', label='SPOTS f051; 126 Myr', color='k')
ax.plot(SPOTS['f085'][0.126]['G_mag']-SPOTS['f085'][0.126]['J_mag'], SPOTS['f085'][0.126]['G_mag'], linewidth=1, linestyle=':', label='SPOTS f085; 126 Myr', color='k')
ax.errorbar(data_obs_Pleiades['G_abs']-data_obs_Pleiades['J_abs'], data_obs_Pleiades['G_abs'], yerr=data_obs_Pleiades['e_g'], fmt='.', zorder=0, color='r', elinewidth=1, capsize=2, alpha=0.125)
ax.legend(fontsize=12, loc='upper center', bbox_to_anchor=(1.25, 0.9))
ax.invert_yaxis()
Tsun = 5772
fig, ax = plt.subplots(1, 1, figsize=(8, 6))
ax.set_ylabel('$A(Li)$ [dex]')
ax.set_xlabel('$T_{eff}$ [K]')
ax.plot(10**SPOTS['f000'][0.126]['log(Teff)'], SPOTS['f000'][0.126]['A(Li)'], linewidth=1, label='SPOTS f000')
ax.plot(10**SPOTS['f017'][0.126]['log(Teff)'], SPOTS['f017'][0.126]['A(Li)'], linewidth=1, label='SPOTS f017')
ax.plot(10**SPOTS['f034'][0.126]['log(Teff)'], SPOTS['f034'][0.126]['A(Li)'], linewidth=1, label='SPOTS f034')
ax.plot(10**SPOTS['f051'][0.126]['log(Teff)'], SPOTS['f051'][0.126]['A(Li)'], linewidth=1, label='SPOTS f051')
ax.errorbar(Tsun, solar_abundance, xerr=0.5, yerr=e_solar_abundance, fmt='.', zorder=2, color='b', elinewidth=1, capsize=0)
ax.plot(BTSettl_Li_isochrones[5]['Teff'], BTSettl_Li_isochrones[5]['A(Li)'], linewidth=1, linestyle='--', color='b')
ax.plot(BTSettl_Li_isochrones[0.120]['Teff'], BTSettl_Li_isochrones[0.120]['A(Li)'], linewidth=1, label='BT-Settl', linestyle='--', color='k')
ax.errorbar(data_obs_Pleiades['Teff'], data_obs_Pleiades['ALi'], yerr=data_obs_Pleiades['e_ALi'], fmt='.', zorder=0, color='r', elinewidth=1, capsize=2)
ax.legend(fontsize=12)
ax.invert_xaxis()
fig, ax = plt.subplots(1, 1, figsize=(8, 6))
ax.set_ylabel('$A(Li)$ [dex]')
ax.set_xlabel('G-J [mag]')
ax.plot(SPOTS['f000'][0.126]['G_mag']-SPOTS['f000'][0.126]['J_mag'], SPOTS['f000'][0.126]['A(Li)'], linewidth=1, label='SPOTS f000')
ax.plot(SPOTS['f017'][0.126]['G_mag']-SPOTS['f000'][0.126]['J_mag'], SPOTS['f017'][0.126]['A(Li)'], linewidth=1, label='SPOTS f017')
ax.plot(SPOTS['f034'][0.126]['G_mag']-SPOTS['f000'][0.126]['J_mag'], SPOTS['f034'][0.126]['A(Li)'], linewidth=1, label='SPOTS f034')
ax.plot(SPOTS['f051'][0.126]['G_mag']-SPOTS['f000'][0.126]['J_mag'], SPOTS['f051'][0.126]['A(Li)'], linewidth=1, label='SPOTS f051')
ax.plot(BTSettl_Li_isochrones[0.120]['G_abs']-BTSettl_Li_isochrones[0.120]['J_abs'], BTSettl_Li_isochrones[0.120]['A(Li)'], linewidth=1, label='BT-Settl', linestyle='--', color='k')
ax.errorbar(data_obs_Pleiades['g']-data_obs_Pleiades['Jmag'], data_obs_Pleiades['ALi'], yerr=data_obs_Pleiades['e_ALi'], fmt='.', zorder=0, color='r', elinewidth=1, capsize=2)
ax.legend(fontsize=12)
<matplotlib.legend.Legend at 0x7f246a1eea10>
data_obs_Pleiades[(~data_obs_Pleiades['ALi'].isnull()) & (data_obs_Pleiades['ALi'] != 0)]['ALi'].count()
99
fig, ax = plt.subplots(1, 1, figsize=(8, 6))
ax.set_ylabel('$A(Li)$ [dex]')
ax.set_xlabel('G-J [mag]')
ax.plot(SPOTS['f017'][0.126]['G_mag']-SPOTS['f017'][0.126]['J_mag'], SPOTS['f017'][0.126]['A(Li)'], linewidth=1, label='SPOTS')
ax.plot(BTSettl_Li_isochrones[0.120]['G_abs']-BTSettl_Li_isochrones[0.120]['J_abs'], BTSettl_Li_isochrones[0.120]['A(Li)'], linewidth=1, label='BT-Settl')
ax.errorbar(data_obs_Pleiades['g']-data_obs_Pleiades['Jmag'], data_obs_Pleiades['ALi'], yerr=data_obs_Pleiades['e_ALi'], fmt='.', zorder=0, color='r', elinewidth=1, capsize=2)
ax.legend()
<matplotlib.legend.Legend at 0x7f247603ea10>
max(10**SPOTS['f000'][0.126]['log(Teff)'])
6512.426277507005
min(10**SPOTS['f000'][0.126]['log(Teff)'])
2949.742462182295
BHAC15¶
file_path = path_all + 'data/BHAC15_iso.GAIA.txt'
BHAC15_dict = models.BHAC15.parse_file(file_path)
csv_file_path = path_all + 'data/BHAC15_iso.GAIA.csv'
models.BHAC15.save_to_csv(BHAC15_dict, file_path)
Corrected photometry SPOTS models¶
plt.rcParams.update({'font.size': 11, 'axes.linewidth': 1, 'axes.edgecolor': 'k'})
plt.rcParams['font.family'] = 'serif'
spots_instance = models.SPOTS_YBC(path_all)
SPOTS_edr3 = spots_instance.SPOTS_edr3
spots_f000_edr3 = spots_instance.spots_f000_edr3
desired_age = 0.120
nearest_age = select_nearest_age(SPOTS_edr3['00'], desired_age)
SPOTS_edr3['00'][nearest_age]
| logAge | Mass | Fspot | Xspot | log(L/Lsun) | log(R/Rsun) | log(g) | log(Teff) | log(T_hot) | log(T_cool) | ... | G-J | G-RP | J-H | H-K | G-H | G-K | G-V | A(Li) | Lsun | Teff | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 8.1 | 0.10 | 0.0 | 0.8 | -2.689492 | -0.761060 | 4.959872 | 3.469784 | 3.469784 | 0.0 | ... | 3.35304 | 1.30207 | 0.59756 | 0.26728 | 0.0 | 4.21788 | -2.47892 | -inf | 0.002044 | 2949.742462 |
| 1 | 8.1 | 0.15 | 0.0 | 0.8 | -2.395952 | -0.669096 | 4.952036 | 3.497187 | 3.497187 | 0.0 | ... | 2.99943 | 1.20822 | 0.60105 | 0.25040 | 0.0 | 3.85088 | -1.73221 | -inf | 0.004018 | 3141.861887 |
| 2 | 8.1 | 0.20 | 0.0 | 0.8 | -2.201137 | -0.599432 | 4.937648 | 3.511059 | 3.511059 | 0.0 | ... | 2.84699 | 1.16439 | 0.60451 | 0.24098 | 0.0 | 3.69248 | -1.43388 | -inf | 0.006293 | 3243.838470 |
| 3 | 8.1 | 0.25 | 0.0 | 0.8 | -2.050513 | -0.543744 | 4.923181 | 3.520871 | 3.520871 | 0.0 | ... | 2.74490 | 1.13382 | 0.60821 | 0.23494 | 0.0 | 3.58805 | -1.28022 | -inf | 0.008902 | 3317.959343 |
| 4 | 8.1 | 0.30 | 0.0 | 0.8 | -1.925703 | -0.496960 | 4.908773 | 3.528682 | 3.528682 | 0.0 | ... | 2.66759 | 1.11005 | 0.61198 | 0.23084 | 0.0 | 3.51041 | -1.18325 | -inf | 0.011866 | 3378.171160 |
| 5 | 8.1 | 0.35 | 0.0 | 0.8 | -1.810935 | -0.454479 | 4.890757 | 3.536133 | 3.536133 | 0.0 | ... | 2.59808 | 1.08829 | 0.61570 | 0.22728 | 0.0 | 3.44106 | -1.10319 | -inf | 0.015455 | 3436.631053 |
| 6 | 8.1 | 0.40 | 0.0 | 0.8 | -1.690418 | -0.414652 | 4.869095 | 3.546349 | 3.546349 | 0.0 | ... | 2.50653 | 1.05926 | 0.62064 | 0.22274 | 0.0 | 3.34991 | -1.00765 | -inf | 0.020398 | 3518.427448 |
| 7 | 8.1 | 0.45 | 0.0 | 0.8 | -1.560148 | -0.373196 | 4.837337 | 3.558189 | 3.558189 | 0.0 | ... | 2.40336 | 1.02586 | 0.62677 | 0.21722 | 0.0 | 3.24735 | -0.92722 | -inf | 0.027533 | 3615.667953 |
| 8 | 8.1 | 0.50 | 0.0 | 0.8 | -1.414063 | -0.329963 | 4.796627 | 3.573093 | 3.573093 | 0.0 | ... | 2.27286 | 0.98262 | 0.63555 | 0.20717 | 0.0 | 3.11558 | -0.81893 | -inf | 0.038542 | 3741.906660 |
| 9 | 8.1 | 0.55 | 0.0 | 0.8 | -1.268473 | -0.291167 | 4.760429 | 3.590093 | 3.590093 | 0.0 | ... | 2.13329 | 0.93498 | 0.64821 | 0.18849 | 0.0 | 2.96999 | -0.68095 | -inf | 0.053892 | 3891.281506 |
| 10 | 8.1 | 0.60 | 0.0 | 0.8 | -1.152988 | -0.260413 | 4.736709 | 3.603587 | 3.603587 | 0.0 | ... | 2.03715 | 0.90024 | 0.65799 | 0.16852 | 0.0 | 2.86366 | -0.61492 | -inf | 0.070309 | 4014.089117 |
| 11 | 8.1 | 0.65 | 0.0 | 0.8 | -1.012628 | -0.226149 | 4.702943 | 3.621545 | 3.621545 | 0.0 | ... | 1.91598 | 0.85397 | 0.66084 | 0.14166 | 0.0 | 2.71848 | -0.55648 | -inf | 0.097134 | 4183.549674 |
| 12 | 8.1 | 0.70 | 0.0 | 0.8 | -0.864977 | -0.195221 | 4.673272 | 3.642994 | 3.642994 | 0.0 | ... | 1.76134 | 0.79178 | 0.64193 | 0.11210 | 0.0 | 2.51537 | -0.46759 | -0.421246 | 0.136466 | 4395.352697 |
| 13 | 8.1 | 0.75 | 0.0 | 0.8 | -0.728511 | -0.169368 | 4.651529 | 3.664184 | 3.664184 | 0.0 | ... | 1.60323 | 0.72951 | 0.58685 | 0.09376 | 0.0 | 2.28384 | -0.40592 | 1.075974 | 0.186848 | 4615.127249 |
| 14 | 8.1 | 0.80 | 0.0 | 0.8 | -0.597804 | -0.145327 | 4.631475 | 3.684840 | 3.684840 | 0.0 | ... | 1.46156 | 0.67394 | 0.51835 | 0.08276 | 0.0 | 2.06267 | -0.34286 | 1.881039 | 0.252462 | 4839.938464 |
| 15 | 8.1 | 0.85 | 0.0 | 0.8 | -0.474711 | -0.121780 | 4.610710 | 3.703839 | 3.703839 | 0.0 | ... | 1.34257 | 0.62656 | 0.46092 | 0.07325 | 0.0 | 1.87674 | -0.30101 | 2.349644 | 0.335189 | 5056.377615 |
| 16 | 8.1 | 0.90 | 0.0 | 0.8 | -0.358181 | -0.098047 | 4.588069 | 3.721106 | 3.721106 | 0.0 | ... | 1.24022 | 0.58621 | 0.41165 | 0.06548 | 0.0 | 1.71735 | -0.26414 | 2.641553 | 0.438348 | 5261.455359 |
| 17 | 8.1 | 0.95 | 0.0 | 0.8 | -0.246858 | -0.073722 | 4.562899 | 3.736774 | 3.736774 | 0.0 | ... | 1.15234 | 0.55188 | 0.36945 | 0.05927 | 0.0 | 1.58106 | -0.24622 | 2.831645 | 0.566424 | 5454.735418 |
| 18 | 8.1 | 1.00 | 0.0 | 0.8 | -0.140140 | -0.048660 | 4.535051 | 3.750922 | 3.750922 | 0.0 | ... | 1.07435 | 0.52027 | 0.33460 | 0.05284 | 0.0 | 1.46179 | -0.22926 | 2.958403 | 0.724203 | 5635.369148 |
| 19 | 8.1 | 1.05 | 0.0 | 0.8 | -0.037916 | -0.022712 | 4.504345 | 3.763504 | 3.763504 | 0.0 | ... | 1.00962 | 0.49384 | 0.30534 | 0.04858 | 0.0 | 1.36354 | -0.20177 | 3.047699 | 0.916397 | 5801.018360 |
| 20 | 8.1 | 1.10 | 0.0 | 0.8 | 0.060162 | 0.003835 | 4.471454 | 3.774750 | 3.774750 | 0.0 | ... | 0.94841 | 0.46791 | 0.27898 | 0.04458 | 0.0 | 1.27197 | -0.17662 | 3.113608 | 1.148582 | 5953.197490 |
| 21 | 8.1 | 1.15 | 0.0 | 0.8 | 0.154305 | 0.029990 | 4.438448 | 3.785208 | 3.785208 | 0.0 | ... | 0.89284 | 0.44356 | 0.25601 | 0.04084 | 0.0 | 1.18969 | -0.17206 | 3.162471 | 1.426608 | 6098.292624 |
| 22 | 8.1 | 1.20 | 0.0 | 0.8 | 0.244443 | 0.055355 | 4.406203 | 3.795061 | 3.795061 | 0.0 | ... | 0.84260 | 0.42131 | 0.23493 | 0.03804 | 0.0 | 1.11557 | -0.16850 | 3.199054 | 1.755672 | 6238.221627 |
| 23 | 8.1 | 1.25 | 0.0 | 0.8 | 0.330458 | 0.079603 | 4.375435 | 3.804440 | 3.804440 | 0.0 | ... | 0.79458 | 0.39976 | 0.21466 | 0.03575 | 0.0 | 1.04499 | -0.16294 | 3.226090 | 2.140217 | 6374.414055 |
| 24 | 8.1 | 1.30 | 0.0 | 0.8 | 0.412322 | 0.101930 | 4.347814 | 3.813743 | 3.813743 | 0.0 | ... | 0.74710 | 0.37807 | 0.19477 | 0.03369 | 0.0 | 0.97556 | -0.15675 | 3.246089 | 2.584178 | 6512.426278 |
25 rows × 60 columns
SPOTS_edr3['85'][nearest_age]
| logAge | Mass | Fspot | Xspot | log(L/Lsun) | log(R/Rsun) | log(g) | log(Teff) | log(T_hot) | log(T_cool) | ... | G-J | G-RP | J-H | H-K | G-H | G-K | G-V | A(Li) | Lsun | Teff | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 8.1 | 0.10 | 0.847 | 0.8 | -2.761755 | -0.665781 | 4.769315 | 3.404079 | 3.479336 | 3.382426 | ... | 4.107711 | 1.451015 | 0.585361 | 0.295944 | 0.0 | 4.989016 | -2.516235 | 2.244433 | 0.001731 | 2535.588947 |
| 1 | 8.1 | 0.15 | 0.847 | 0.8 | -2.497531 | -0.574975 | 4.763794 | 3.424732 | 3.499990 | 3.403080 | ... | 4.075011 | 1.437369 | 0.585551 | 0.295543 | 0.0 | 4.956105 | -1.866302 | -inf | 0.003180 | 2659.084019 |
| 2 | 8.1 | 0.20 | 0.847 | 0.8 | -2.314691 | -0.509727 | 4.758237 | 3.437818 | 3.513075 | 3.416165 | ... | 4.010649 | 1.421087 | 0.586131 | 0.294935 | 0.0 | 4.891715 | -1.554233 | -inf | 0.004845 | 2740.425311 |
| 3 | 8.1 | 0.25 | 0.847 | 0.8 | -2.172126 | -0.459132 | 4.753958 | 3.448162 | 3.523419 | 3.426509 | ... | 3.872527 | 1.393568 | 0.587446 | 0.293379 | 0.0 | 4.753352 | -1.426080 | -inf | 0.006728 | 2806.479796 |
| 4 | 8.1 | 0.30 | 0.847 | 0.8 | -2.049000 | -0.416230 | 4.747312 | 3.457492 | 3.532750 | 3.435840 | ... | 3.733294 | 1.366071 | 0.588768 | 0.290341 | 0.0 | 4.612402 | -1.356427 | -inf | 0.008933 | 2867.426272 |
| 5 | 8.1 | 0.35 | 0.847 | 0.8 | -1.931932 | -0.376595 | 4.734990 | 3.466942 | 3.542199 | 3.445289 | ... | 3.600167 | 1.338875 | 0.589996 | 0.286253 | 0.0 | 4.476416 | -1.302754 | -inf | 0.011697 | 2930.501311 |
| 6 | 8.1 | 0.40 | 0.847 | 0.8 | -1.809499 | -0.338319 | 4.716429 | 3.478412 | 3.553670 | 3.456760 | ... | 3.439087 | 1.303660 | 0.591724 | 0.280542 | 0.0 | 4.311352 | -1.260680 | -inf | 0.015506 | 3008.929900 |
| 7 | 8.1 | 0.45 | 0.847 | 0.8 | -1.671891 | -0.297670 | 4.686284 | 3.492490 | 3.567747 | 3.470837 | ... | 3.254961 | 1.260181 | 0.594213 | 0.271423 | 0.0 | 4.120597 | -1.195125 | -inf | 0.021287 | 3108.062046 |
| 8 | 8.1 | 0.50 | 0.847 | 0.8 | -1.501147 | -0.253464 | 4.643629 | 3.513072 | 3.588330 | 3.491420 | ... | 2.995661 | 1.192625 | 0.601096 | 0.255155 | 0.0 | 3.851911 | -0.991065 | 0.048188 | 0.031539 | 3258.910549 |
| 9 | 8.1 | 0.55 | 0.847 | 0.8 | -1.289990 | -0.212640 | 4.603374 | 3.545450 | 3.620707 | 3.523797 | ... | 2.677116 | 1.102191 | 0.622722 | 0.229291 | 0.0 | 3.529129 | -0.728217 | 1.942069 | 0.051287 | 3511.151994 |
| 10 | 8.1 | 0.60 | 0.847 | 0.8 | -1.103566 | -0.197423 | 4.610728 | 3.584447 | 3.659705 | 3.562795 | ... | 2.323397 | 0.990639 | 0.643738 | 0.207503 | 0.0 | 3.174638 | -0.534171 | 2.641929 | 0.078783 | 3841.026027 |
| 11 | 8.1 | 0.65 | 0.847 | 0.8 | -1.035662 | -0.190024 | 4.630692 | 3.597724 | 3.672981 | 3.576071 | ... | 2.206161 | 0.951923 | 0.648395 | 0.196717 | 0.0 | 3.051273 | -0.470558 | 2.935574 | 0.092117 | 3960.258456 |
| 12 | 8.1 | 0.70 | 0.847 | 0.8 | -0.893310 | -0.162198 | 4.607226 | 3.619399 | 3.694656 | 3.597746 | ... | 2.039102 | 0.893324 | 0.663340 | 0.168677 | 0.0 | 2.871119 | -0.380797 | 3.080684 | 0.127847 | 4162.927620 |
| 13 | 8.1 | 0.75 | 0.847 | 0.8 | -0.749999 | -0.133807 | 4.580406 | 3.641031 | 3.716288 | 3.619378 | ... | 1.891602 | 0.837590 | 0.663877 | 0.137911 | 0.0 | 2.693391 | -0.337469 | 3.157947 | 0.177828 | 4375.532359 |
| 14 | 8.1 | 0.80 | 0.847 | 0.8 | -0.619289 | -0.104438 | 4.549698 | 3.659024 | 3.734282 | 3.637372 | ... | 1.761485 | 0.785528 | 0.648344 | 0.113136 | 0.0 | 2.522965 | -0.304589 | 3.201491 | 0.240276 | 4560.623177 |
| 15 | 8.1 | 0.85 | 0.847 | 0.8 | -0.494575 | -0.073767 | 4.514684 | 3.674867 | 3.750125 | 3.653215 | ... | 1.643328 | 0.739492 | 0.612932 | 0.098517 | 0.0 | 2.354777 | -0.292595 | 3.225745 | 0.320202 | 4730.064360 |
| 16 | 8.1 | 0.90 | 0.847 | 0.8 | -0.375724 | -0.041868 | 4.475710 | 3.688631 | 3.763888 | 3.666978 | ... | 1.544597 | 0.701406 | 0.568451 | 0.089794 | 0.0 | 2.202842 | -0.267726 | 3.239739 | 0.420995 | 4882.368444 |
| 17 | 8.1 | 0.95 | 0.847 | 0.8 | -0.261217 | -0.009255 | 4.433965 | 3.700951 | 3.776208 | 3.679298 | ... | 1.460212 | 0.668364 | 0.525453 | 0.083907 | 0.0 | 2.069572 | -0.246427 | 3.252158 | 0.548003 | 5022.854178 |
| 18 | 8.1 | 1.00 | 0.847 | 0.8 | -0.150509 | 0.023071 | 4.391589 | 3.712464 | 3.787722 | 3.690812 | ... | 1.388522 | 0.639261 | 0.491256 | 0.077892 | 0.0 | 1.957670 | -0.237135 | 3.263712 | 0.707116 | 5157.797769 |
| 19 | 8.1 | 1.05 | 0.847 | 0.8 | -0.044366 | 0.054240 | 4.350440 | 3.723416 | 3.798673 | 3.701763 | ... | 1.321207 | 0.612244 | 0.458590 | 0.072423 | 0.0 | 1.852219 | -0.227984 | 3.273507 | 0.902888 | 5289.511674 |
| 20 | 8.1 | 1.10 | 0.847 | 0.8 | 0.056709 | 0.083544 | 4.312036 | 3.734032 | 3.809290 | 3.712380 | ... | 1.258516 | 0.587548 | 0.427405 | 0.068161 | 0.0 | 1.754081 | -0.213987 | 3.280966 | 1.139485 | 5420.412773 |
| 21 | 8.1 | 1.15 | 0.847 | 0.8 | 0.152666 | 0.109666 | 4.279098 | 3.744961 | 3.820219 | 3.723309 | ... | 1.196089 | 0.563002 | 0.397408 | 0.063248 | 0.0 | 1.656745 | -0.207143 | 3.286534 | 1.421237 | 5558.543901 |
| 22 | 8.1 | 1.20 | 0.847 | 0.8 | 0.243698 | 0.131549 | 4.253815 | 3.756777 | 3.832035 | 3.735125 | ... | 1.130216 | 0.537072 | 0.366100 | 0.058241 | 0.0 | 1.554558 | -0.198745 | 3.290561 | 1.752660 | 5711.855868 |
| 23 | 8.1 | 1.25 | 0.847 | 0.8 | 0.330066 | 0.148334 | 4.237973 | 3.769977 | 3.845234 | 3.748324 | ... | 1.059353 | 0.508874 | 0.333063 | 0.053028 | 0.0 | 1.445444 | -0.187981 | 3.293454 | 2.138289 | 5888.121595 |
| 24 | 8.1 | 1.30 | 0.847 | 0.8 | 0.412044 | 0.152254 | 4.247167 | 3.788511 | 3.863769 | 3.766859 | ... | 0.962256 | 0.469227 | 0.289012 | 0.046617 | 0.0 | 1.297885 | -0.164622 | 3.295477 | 2.582520 | 6144.850624 |
25 rows × 60 columns
SPOTS_edr3['34'][nearest_age]
| logAge | Mass | Fspot | Xspot | log(L/Lsun) | log(R/Rsun) | log(g) | log(Teff) | log(T_hot) | log(T_cool) | ... | G-J | G-RP | J-H | H-K | G-H | G-K | G-V | A(Li) | Lsun | Teff | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 8.1 | 0.10 | 0.339 | 0.8 | -2.712418 | -0.730690 | 4.899133 | 3.448868 | 3.473095 | 3.376185 | ... | 3.742510 | 1.367287 | 0.592289 | 0.279062 | 0.0 | 4.613860 | -2.225280 | -inf | 0.001939 | 2811.043719 |
| 1 | 8.1 | 0.15 | 0.339 | 0.8 | -2.429194 | -0.639035 | 4.891914 | 3.473846 | 3.498074 | 3.401164 | ... | 3.552493 | 1.303907 | 0.593885 | 0.273108 | 0.0 | 4.419486 | -1.452944 | -inf | 0.003722 | 2977.461880 |
| 2 | 8.1 | 0.20 | 0.339 | 0.8 | -2.239438 | -0.570778 | 4.880339 | 3.487157 | 3.511384 | 3.414474 | ... | 3.449597 | 1.270577 | 0.595468 | 0.270133 | 0.0 | 4.315199 | -1.144710 | -inf | 0.005762 | 3070.130315 |
| 3 | 8.1 | 0.25 | 0.339 | 0.8 | -2.093052 | -0.516734 | 4.869161 | 3.496731 | 3.520959 | 3.424049 | ... | 3.346588 | 1.245101 | 0.597407 | 0.268085 | 0.0 | 4.212080 | -1.002123 | -inf | 0.008071 | 3138.565164 |
| 4 | 8.1 | 0.30 | 0.339 | 0.8 | -1.970007 | -0.470971 | 4.856795 | 3.504611 | 3.528839 | 3.431929 | ... | 3.251364 | 1.223920 | 0.599437 | 0.266240 | 0.0 | 4.117042 | -0.921175 | -inf | 0.010715 | 3196.032449 |
| 5 | 8.1 | 0.35 | 0.339 | 0.8 | -1.855306 | -0.429144 | 4.840087 | 3.512373 | 3.536600 | 3.439690 | ... | 3.164504 | 1.204025 | 0.601326 | 0.263619 | 0.0 | 4.029448 | -0.853239 | -inf | 0.013954 | 3253.664509 |
| 6 | 8.1 | 0.40 | 0.339 | 0.8 | -1.732767 | -0.389883 | 4.819557 | 3.523377 | 3.547604 | 3.450694 | ... | 3.043895 | 1.175900 | 0.604096 | 0.259253 | 0.0 | 3.907245 | -0.775352 | -inf | 0.018503 | 3337.158468 |
| 7 | 8.1 | 0.45 | 0.339 | 0.8 | -1.598062 | -0.348402 | 4.787747 | 3.536313 | 3.560540 | 3.463630 | ... | 2.903786 | 1.141178 | 0.607729 | 0.253016 | 0.0 | 3.764531 | -0.716838 | -inf | 0.025231 | 3438.053813 |
| 8 | 8.1 | 0.50 | 0.339 | 0.8 | -1.440761 | -0.304085 | 4.744871 | 3.553480 | 3.577707 | 3.480797 | ... | 2.725873 | 1.093817 | 0.613757 | 0.240962 | 0.0 | 3.580592 | -0.606917 | -inf | 0.036244 | 3576.675831 |
| 9 | 8.1 | 0.55 | 0.339 | 0.8 | -1.270122 | -0.264206 | 4.706506 | 3.576200 | 3.600427 | 3.503517 | ... | 2.512445 | 1.032694 | 0.626376 | 0.218399 | 0.0 | 3.357220 | -0.474141 | -inf | 0.053688 | 3768.772046 |
| 10 | 8.1 | 0.60 | 0.339 | 0.8 | -1.153239 | -0.239203 | 4.694288 | 3.592919 | 3.617146 | 3.520236 | ... | 2.374117 | 0.990087 | 0.633704 | 0.201647 | 0.0 | 3.209468 | -0.431123 | -1.097940 | 0.070269 | 3916.686310 |
| 11 | 8.1 | 0.65 | 0.339 | 0.8 | -1.031144 | -0.210744 | 4.672133 | 3.609213 | 3.633441 | 3.536531 | ... | 2.241838 | 0.946093 | 0.637087 | 0.188190 | 0.0 | 3.067116 | -0.360417 | 0.878639 | 0.093080 | 4066.430555 |
| 12 | 8.1 | 0.70 | 0.339 | 0.8 | -0.876848 | -0.181250 | 4.645329 | 3.633040 | 3.657268 | 3.560358 | ... | 2.047628 | 0.878765 | 0.628806 | 0.172535 | 0.0 | 2.848968 | -0.299951 | 1.935785 | 0.132786 | 4295.761882 |
| 13 | 8.1 | 0.75 | 0.339 | 0.8 | -0.736951 | -0.157424 | 4.627641 | 3.656102 | 3.680329 | 3.583419 | ... | 1.868222 | 0.815837 | 0.611799 | 0.155998 | 0.0 | 2.636019 | -0.230109 | 2.463906 | 0.183252 | 4530.035861 |
| 14 | 8.1 | 0.80 | 0.339 | 0.8 | -0.605662 | -0.132469 | 4.605759 | 3.676446 | 3.700674 | 3.603764 | ... | 1.731855 | 0.765344 | 0.601383 | 0.134222 | 0.0 | 2.467459 | -0.192590 | 2.756017 | 0.247935 | 4747.295001 |
| 15 | 8.1 | 0.85 | 0.339 | 0.8 | -0.481992 | -0.107274 | 4.581698 | 3.694766 | 3.718994 | 3.622084 | ... | 1.618071 | 0.721858 | 0.583690 | 0.114929 | 0.0 | 2.316690 | -0.163635 | 2.929287 | 0.329615 | 4951.834257 |
| 16 | 8.1 | 0.90 | 0.339 | 0.8 | -0.364868 | -0.081453 | 4.554880 | 3.711137 | 3.735364 | 3.638454 | ... | 1.514684 | 0.681663 | 0.561779 | 0.097240 | 0.0 | 2.173704 | -0.159854 | 3.039105 | 0.431650 | 5142.056686 |
| 17 | 8.1 | 0.95 | 0.339 | 0.8 | -0.252749 | -0.054619 | 4.524694 | 3.725750 | 3.749977 | 3.653067 | ... | 1.421049 | 0.645613 | 0.528213 | 0.086146 | 0.0 | 2.035408 | -0.159072 | 3.110233 | 0.558793 | 5318.017351 |
| 18 | 8.1 | 1.00 | 0.339 | 0.8 | -0.144951 | -0.026781 | 4.491293 | 3.738780 | 3.763008 | 3.666098 | ... | 1.341370 | 0.615109 | 0.490456 | 0.078869 | 0.0 | 1.910695 | -0.147280 | 3.159421 | 0.716224 | 5479.993836 |
| 19 | 8.1 | 1.05 | 0.339 | 0.8 | -0.041363 | 0.001673 | 4.455574 | 3.750450 | 3.774677 | 3.677767 | ... | 1.270744 | 0.587172 | 0.454556 | 0.073601 | 0.0 | 1.798901 | -0.133883 | 3.194028 | 0.909154 | 5629.242268 |
| 20 | 8.1 | 1.10 | 0.339 | 0.8 | 0.058102 | 0.029655 | 4.419813 | 3.761325 | 3.785553 | 3.688643 | ... | 1.208803 | 0.561706 | 0.424804 | 0.068538 | 0.0 | 1.702145 | -0.137937 | 3.219617 | 1.143146 | 5771.983488 |
| 21 | 8.1 | 1.15 | 0.339 | 0.8 | 0.153256 | 0.056658 | 4.385114 | 3.771612 | 3.795840 | 3.698930 | ... | 1.151474 | 0.538113 | 0.397171 | 0.063878 | 0.0 | 1.612523 | -0.141929 | 3.239359 | 1.423167 | 5910.339933 |
| 22 | 8.1 | 1.20 | 0.339 | 0.8 | 0.243977 | 0.082324 | 4.352265 | 3.781460 | 3.805687 | 3.708777 | ... | 1.097759 | 0.516154 | 0.370454 | 0.060524 | 0.0 | 1.528737 | -0.142472 | 3.255346 | 1.753789 | 6045.883439 |
| 23 | 8.1 | 1.25 | 0.339 | 0.8 | 0.330256 | 0.105689 | 4.323264 | 3.791347 | 3.815575 | 3.718665 | ... | 1.044562 | 0.494133 | 0.345278 | 0.056602 | 0.0 | 1.446442 | -0.141702 | 3.267539 | 2.139224 | 6185.104762 |
| 24 | 8.1 | 1.30 | 0.339 | 0.8 | 0.412254 | 0.125899 | 4.299877 | 3.801741 | 3.825969 | 3.729059 | ... | 0.990369 | 0.471804 | 0.319197 | 0.053157 | 0.0 | 1.362724 | -0.139495 | 3.276616 | 2.583771 | 6334.923139 |
25 rows × 60 columns
spots_f000_edr3.columns
Index(['logAge', 'Mass', 'Fspot', 'Xspot', 'log(L/Lsun)', 'log(R/Rsun)',
'log(g)', 'log(Teff)', 'log(T_hot)', 'log(T_cool)', 'TauCZ', 'Li/Li0',
'B_mag', 'V_mag', 'Rc_mag', 'Ic_mag', 'J_mag', 'H_mag', 'K_mag',
'W1_mag', 'G_mag', 'BP_mag', 'RP_mag', 'Thot', 'Tcool', 'Label_0',
'J_hot', 'H_hot', 'Ks_hot', 'Label_1', 'J_cool', 'H_cool', 'Ks_cool',
'Label_2', 'G_hot', 'G_BP_hot', 'G_RP_hot', 'Label_3', 'G_cool',
'G_BP_cool', 'G_RP_cool'],
dtype='object')
#for age in SPOTS_edr3['00'].keys():
# print(age)
# print(min(SPOTS_edr3['00'][age]['Teff']))
for f in SPOTS_edr3.keys():
print(f)
00 17 34 51 68 85
SPOTS_edr3['00'].keys()
dict_keys([0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.011, 0.013, 0.014, 0.016, 0.018, 0.02, 0.022, 0.025, 0.028, 0.032, 0.035, 0.04, 0.045, 0.05, 0.056, 0.063, 0.071, 0.079, 0.089, 0.1, 0.112, 0.126, 0.141, 0.158, 0.178, 0.2, 0.224, 0.251, 0.282, 0.316, 0.355, 0.398, 0.447, 0.501, 0.562, 0.631, 0.708, 0.794, 0.891, 1.0, 1.122, 1.259, 1.413, 1.585, 1.778, 1.995, 2.239, 2.512, 2.818, 3.162, 3.548, 3.981])
SPOTS_edr3['00'][nearest_age].columns
Index(['logAge', 'Mass', 'Fspot', 'Xspot', 'log(L/Lsun)', 'log(R/Rsun)',
'log(g)', 'log(Teff)', 'log(T_hot)', 'log(T_cool)', 'TauCZ', 'Li/Li0',
'B_mag', 'V_mag', 'Rc_mag', 'Ic_mag', 'J_mag', 'H_mag', 'K_mag',
'W1_mag', 'G_mag', 'BP_mag', 'RP_mag', 'Thot', 'Tcool', 'Label_0',
'J_hot', 'H_hot', 'Ks_hot', 'Label_1', 'J_cool', 'H_cool', 'Ks_cool',
'Label_2', 'G_hot', 'G_BP_hot', 'G_RP_hot', 'Label_3', 'G_cool',
'G_BP_cool', 'G_RP_cool', 'Age_Gyr', 'BP_abs', 'RP_abs', 'G_abs',
'J_abs', 'H_abs', 'K_abs', 'M/Ms', 'BP-RP', 'G-J', 'G-RP', 'J-H', 'H-K',
'G-H', 'G-K', 'G-V', 'A(Li)', 'Lsun', 'Teff'],
dtype='object')
fig, ax = plt.subplots(1, 1, figsize=(8, 6))
ax.set_ylabel('$A(Li)$ [dex]')
ax.set_xlabel('$B_p-R_p$ [mag]')
ax.plot(SPOTS_edr3['00'][0.025]['BP-RP'], SPOTS_edr3['00'][0.025]['A(Li)'], linewidth=1, label='SPOTS-YBC f000; 25 Myr')
ax.plot(SPOTS_edr3['17'][0.006]['BP-RP'], SPOTS_edr3['17'][0.006]['A(Li)'], linewidth=1, label='SPOTS-YBC f017; 6 Myr')
ax.plot(SPOTS_edr3['34'][0.010]['BP-RP'], SPOTS_edr3['34'][0.010]['A(Li)'], linewidth=1, label='SPOTS-YBC f034; 10 Myr')
ax.plot(SPOTS_edr3['51'][0.016]['BP-RP'], SPOTS_edr3['51'][0.016]['A(Li)'], linewidth=1, label='SPOTS-YBC f051; 16 Myr')
ax.plot(SPOTS_edr3['68'][0.028]['BP-RP'], SPOTS_edr3['68'][0.028]['A(Li)'], linewidth=1, label='SPOTS-YBC f068; 28 Myr')
ax.plot(SPOTS_edr3['85'][0.035]['BP-RP'], SPOTS_edr3['85'][0.035]['A(Li)'], linewidth=1, label='SPOTS-YBC f085; 35 Myr')
ax.plot(SPOTS_edr3['00'][0.126]['BP-RP'], SPOTS_edr3['00'][0.126]['A(Li)'], linewidth=1, color = 'k', linestyle='--', label='SPOTS-YBC f000; 126 Myr')
ax.errorbar(data_obs_Pleiades['bp']-data_obs_Pleiades['rp'], data_obs_Pleiades['ALi'], yerr=data_obs_Pleiades['e_ALi'], fmt='.', zorder=0, color='r', elinewidth=1, capsize=2)
ax.legend(fontsize=12, loc='upper center', bbox_to_anchor=(1.25, 0.9))
<matplotlib.legend.Legend at 0x7f2476ff6ad0>
fig, ax = plt.subplots(1, 1, figsize=(8, 6))
ax.set_ylabel('$A(Li)$ [dex]')
ax.set_xlabel('$G-J$ [mag]')
ax.plot(SPOTS_edr3['00'][0.025]['G-J'], SPOTS_edr3['00'][0.025]['A(Li)'], linewidth=1, label='SPOTS-YBC f000; 25 Myr')
ax.plot(SPOTS_edr3['17'][0.006]['G-J'], SPOTS_edr3['17'][0.006]['A(Li)'], linewidth=1, label='SPOTS-YBC f017; 6 Myr')
ax.plot(SPOTS_edr3['34'][0.010]['G-J'], SPOTS_edr3['34'][0.010]['A(Li)'], linewidth=1, label='SPOTS-YBC f034; 10 Myr')
ax.plot(SPOTS_edr3['51'][0.016]['G-J'], SPOTS_edr3['51'][0.016]['A(Li)'], linewidth=1, label='SPOTS-YBC f051; 16 Myr')
ax.plot(SPOTS_edr3['68'][0.028]['G-J'], SPOTS_edr3['68'][0.028]['A(Li)'], linewidth=1, label='SPOTS-YBC f068; 28 Myr')
ax.plot(SPOTS_edr3['85'][0.035]['G-J'], SPOTS_edr3['85'][0.035]['A(Li)'], linewidth=1, label='SPOTS-YBC f085; 35 Myr')
ax.plot(SPOTS_edr3['00'][0.126]['G-J'], SPOTS_edr3['00'][0.126]['A(Li)'], linewidth=1, color = 'k', linestyle='--', label='SPOTS-YBC f000; 126 Myr')
ax.plot(SPOTS_edr3['85'][0.126]['G-J'], SPOTS_edr3['85'][0.126]['A(Li)'], linewidth=1, color = 'k', linestyle=':', label='SPOTS-YBC f085; 126 Myr')
ax.errorbar(data_obs_Pleiades['g']-data_obs_Pleiades['Jmag'], data_obs_Pleiades['ALi'], yerr=data_obs_Pleiades['e_ALi'], fmt='.', zorder=0, color='r', elinewidth=1, capsize=2)
ax.legend(fontsize=12, loc='upper center', bbox_to_anchor=(1.25, 0.9))
<matplotlib.legend.Legend at 0x7f247d161ad0>
fig, ax = plt.subplots(1, 1, figsize=(8, 6))
ax.set_ylabel('$A(Li)$ [dex]')
ax.set_xlabel('$T_{eff}$ [K]')
ax.plot(SPOTS_edr3['00'][0.056]['Teff'], SPOTS_edr3['00'][0.056]['A(Li)'], linewidth=1, color='orange', label='SPOTS-YBC f000; 56 Myr')
ax.plot(SPOTS_edr3['17'][0.056]['Teff'], SPOTS_edr3['17'][0.056]['A(Li)'], linewidth=1, linestyle='--', color='orange', label='SPOTS-YBC f017; 56 Myr')
ax.plot(SPOTS_edr3['85'][0.056]['Teff'], SPOTS_edr3['51'][0.056]['A(Li)'], linewidth=1, linestyle=':', color='orange', label='SPOTS-YBC f085; 50 Myr')
ax.plot(SPOTS_edr3['00'][0.05]['Teff'], SPOTS_edr3['00'][0.05]['A(Li)'], linewidth=1, color='r', label='SPOTS-YBC f000; 50 Myr')
ax.plot(SPOTS_edr3['17'][0.05]['Teff'], SPOTS_edr3['17'][0.05]['A(Li)'], linewidth=1, linestyle='--', color='r', label='SPOTS-YBC f017; 50 Myr')
ax.plot(SPOTS_edr3['85'][0.05]['Teff'], SPOTS_edr3['51'][0.05]['A(Li)'], linewidth=1, linestyle=':', color='r', label='SPOTS-YBC f085; 50 Myr')
ax.plot(SPOTS_edr3['00'][0.126]['Teff'], SPOTS_edr3['00'][0.126]['A(Li)'], linewidth=1, color = 'k', label='SPOTS-YBC f000; 126 Myr')
ax.plot(SPOTS_edr3['17'][0.126]['Teff'], SPOTS_edr3['17'][0.126]['A(Li)'], linewidth=1, color = 'k', linestyle='--', label='SPOTS-YBC f017; 126 Myr')
ax.plot(SPOTS_edr3['85'][0.126]['Teff'], SPOTS_edr3['85'][0.126]['A(Li)'], linewidth=1, color = 'k', linestyle=':', label='SPOTS-YBC f085; 126 Myr')
ax.invert_xaxis()
#ax.errorbar(data_obs_Pleiades['Teff'], data_obs_Pleiades['ALi'], yerr=data_obs_Pleiades['e_ALi'], fmt='.', zorder=0, color='r', elinewidth=1, capsize=2)
ax.legend(fontsize=12, loc='upper center', bbox_to_anchor=(1.25, 0.9))
<matplotlib.legend.Legend at 0x7f2484c15750>
fig, ax = plt.subplots(1, 1, figsize=(8, 6))
ax.set_ylabel('$G$ [mag]')
ax.set_xlabel('$B_p-R_p$ [mag]')
ax.plot(SPOTS_edr3['00'][0.126]['BP_abs']-SPOTS_edr3['00'][0.126]['RP_abs'], SPOTS_edr3['00'][0.126]['G_abs'], linewidth=1, label='SPOTS-YBC f000; 126 Myr', color='k')
ax.plot(SPOTS_edr3['17'][0.126]['BP_abs']-SPOTS_edr3['17'][0.126]['RP_abs'], SPOTS_edr3['17'][0.126]['G_abs'], linewidth=1, label='SPOTS-YBC f017; 126 Myr', color='r')
ax.plot(SPOTS_edr3['34'][0.126]['BP_abs']-SPOTS_edr3['34'][0.126]['RP_abs'], SPOTS_edr3['34'][0.126]['G_abs'], linewidth=1, label='SPOTS-YBC f034; 126 Myr', color='orange')
ax.plot(SPOTS_edr3['51'][0.126]['BP_abs']-SPOTS_edr3['51'][0.126]['RP_abs'], SPOTS_edr3['51'][0.126]['G_abs'], linewidth=1, label='SPOTS-YBC f051; 126 Myr', color='green')
ax.plot(SPOTS_edr3['85'][0.126]['BP_abs']-SPOTS_edr3['85'][0.126]['RP_abs'], SPOTS_edr3['85'][0.126]['G_abs'], linewidth=1, label='SPOTS-YBC f085; 126 Myr', color='blue')
ax.errorbar(data_obs_Pleiades['BP_abs']-data_obs_Pleiades['RP_abs'], data_obs_Pleiades['G_abs'], yerr=data_obs_Pleiades['e_g'], fmt='.', zorder=0, color='r', elinewidth=1, capsize=2, alpha=0.125)
ax.legend(fontsize=12, loc='upper center', bbox_to_anchor=(1.25, 0.9))
ax.invert_yaxis()
fig, ax = plt.subplots(1, 1, figsize=(8, 6))
ax.set_ylabel('$G$ [mag]')
ax.set_xlabel('$G-J$ [mag]')
ax.plot(SPOTS_edr3['00'][0.126]['G_abs']-SPOTS_edr3['00'][0.126]['J_abs'], SPOTS_edr3['00'][0.126]['G_abs'], linewidth=1, label='SPOTS-YBC f000; 126 Myr', color='k')
ax.plot(SPOTS_edr3['17'][0.126]['G_abs']-SPOTS_edr3['17'][0.126]['J_abs'], SPOTS_edr3['17'][0.126]['G_abs'], linewidth=1, label='SPOTS-YBC f017; 126 Myr', color='r')
ax.plot(SPOTS_edr3['34'][0.126]['G_abs']-SPOTS_edr3['34'][0.126]['J_abs'], SPOTS_edr3['34'][0.126]['G_abs'], linewidth=1, label='SPOTS-YBC f034; 126 Myr', color='orange')
ax.plot(SPOTS_edr3['51'][0.126]['G_abs']-SPOTS_edr3['51'][0.126]['J_abs'], SPOTS_edr3['51'][0.126]['G_abs'], linewidth=1, label='SPOTS-YBC f051; 126 Myr', color='green')
ax.plot(SPOTS_edr3['85'][0.126]['G_abs']-SPOTS_edr3['85'][0.126]['J_abs'], SPOTS_edr3['85'][0.126]['G_abs'], linewidth=1, label='SPOTS-YBC f085; 126 Myr', color='blue')
ax.errorbar(data_obs_Pleiades['G_abs']-data_obs_Pleiades['J_abs'], data_obs_Pleiades['G_abs'], yerr=data_obs_Pleiades['e_g'], fmt='.', zorder=0, color='r', elinewidth=1, capsize=2, alpha=0.125)
ax.legend(fontsize=12, loc='upper center', bbox_to_anchor=(1.25, 0.9))
ax.invert_yaxis()
fig, ax = plt.subplots(1, 1, figsize=(8, 6))
ax.set_ylabel('$G$ [mag]')
ax.set_xlabel('$G-R_p$ [mag]')
ax.plot(SPOTS_edr3['00'][0.126]['G_abs']-SPOTS_edr3['00'][0.126]['RP_abs'], SPOTS_edr3['00'][0.126]['G_abs'], linewidth=1, label='SPOTS-YBC f000; 126 Myr', color='k')
ax.plot(SPOTS_edr3['17'][0.126]['G_abs']-SPOTS_edr3['17'][0.126]['RP_abs'], SPOTS_edr3['17'][0.126]['G_abs'], linewidth=1, label='SPOTS-YBC f017; 126 Myr', color='r')
ax.plot(SPOTS_edr3['34'][0.126]['G_abs']-SPOTS_edr3['34'][0.126]['RP_abs'], SPOTS_edr3['34'][0.126]['G_abs'], linewidth=1, label='SPOTS-YBC f034; 126 Myr', color='orange')
ax.plot(SPOTS_edr3['51'][0.126]['G_abs']-SPOTS_edr3['51'][0.126]['RP_abs'], SPOTS_edr3['51'][0.126]['G_abs'], linewidth=1, label='SPOTS-YBC f051; 126 Myr', color='green')
ax.plot(SPOTS_edr3['85'][0.126]['G_abs']-SPOTS_edr3['85'][0.126]['RP_abs'], SPOTS_edr3['85'][0.126]['G_abs'], linewidth=1, label='SPOTS-YBC f085; 126 Myr', color='blue')
ax.errorbar(data_obs_Pleiades['G_abs']-data_obs_Pleiades['RP_abs'], data_obs_Pleiades['G_abs'], yerr=data_obs_Pleiades['e_g'], fmt='.', zorder=0, color='r', elinewidth=1, capsize=2, alpha=0.125)
ax.legend(fontsize=12, loc='upper center', bbox_to_anchor=(1.25, 0.9))
ax.invert_yaxis()
ax.set_xlim(0, 1.5)
(0.0, 1.5)
SPOTS_edr3['00'][0.126]['Teff']
0 2949.742462 1 3141.861887 2 3243.838470 3 3317.959343 4 3378.171160 5 3436.631053 6 3518.427448 7 3615.667953 8 3741.906660 9 3891.281506 10 4014.089117 11 4183.549674 12 4395.352697 13 4615.127249 14 4839.938464 15 5056.377615 16 5261.455359 17 5454.735418 18 5635.369148 19 5801.018360 20 5953.197490 21 6098.292624 22 6238.221627 23 6374.414055 24 6512.426278 Name: Teff, dtype: float64
fig, ax = plt.subplots(1, 1, figsize=(8, 6))
ax.set_ylabel('$\log{(\mathcal{L}/\mathcal{L}_{\odot})}$')
ax.set_xlabel('$T_{eff}$ [K]')
ax.plot(SPOTS_edr3['00'][0.126]['Teff'], SPOTS_edr3['00'][0.126]['log(L/Lsun)'], linewidth=1, label='SPOTS-YBC f000; 126 Myr', color='k')
ax.plot(SPOTS_edr3['17'][0.126]['Teff'], SPOTS_edr3['17'][0.126]['log(L/Lsun)'], linewidth=1, label='SPOTS-YBC f017; 126 Myr', color='r')
ax.plot(SPOTS_edr3['34'][0.126]['Teff'], SPOTS_edr3['34'][0.126]['log(L/Lsun)'], linewidth=1, label='SPOTS-YBC f034; 126 Myr', color='orange')
ax.plot(SPOTS_edr3['51'][0.126]['Teff'], SPOTS_edr3['51'][0.126]['log(L/Lsun)'], linewidth=1, label='SPOTS-YBC f051; 126 Myr', color='green')
ax.plot(SPOTS_edr3['85'][0.126]['Teff'], SPOTS_edr3['85'][0.126]['log(L/Lsun)'], linewidth=1, label='SPOTS-YBC f085; 126 Myr', color='blue')
ax.scatter(data_obs_Pleiades['Teff_x'], data_obs_Pleiades['log(L/Lsun)'], zorder=0, color='r', s=10, alpha=0.125)
ax.legend(fontsize=12, loc='upper center', bbox_to_anchor=(1.25, 0.9))
ax.invert_xaxis()
from models_test import PlotAnalyzer
BTSettl_Li_isochrones_Teff.columns
Index(['age_Gyr', 't(Gyr)', 'M/Ms', 'Teff', 'log(L/Lsun)', 'lg(g)', 'R(Gcm)',
'D', 'Li', 'G_abs', 'BP_abs', 'RP_abs', 'J_abs', 'H_abs', 'K_abs',
'g_abs', 'r_abs', 'i_abs', 'y_abs', 'z_abs', 'A(Li)', 'Lsun'],
dtype='object')
plot_analyzer = PlotAnalyzer(path_all)
SPOTS = plot_analyzer.SPOTS_edr3
data_obs = data_obs_Pleiades
band_1 = 'BP_abs'
band_2 = 'RP_abs'
band_y = 'G_abs'
age_iso = 0.120
max_mag = 3.5
intervals, interval_x, x, y, e_x, e_y = plot_analyzer.plot_process_CMD(SPOTS, data_obs, band_1, band_2, band_y, age_iso, max_mag)
SPOTS_iso = plot_analyzer.plot_result(interval_x, x, y, e_x, e_y, SPOTS, band_1, band_2, band_y, data_obs, age_iso, max_mag, l=2, BTSettl=True)
plot_analyzer = PlotAnalyzer(path_all)
SPOTS = plot_analyzer.SPOTS_edr3
data_obs = data_obs_Pleiades
band_1 = 'G_abs'
band_2 = 'J_abs'
band_y = 'G_abs'
age_iso = 0.120
max_mag = 3.5
intervals_J, interval_x_J, x_J, y_J, e_x_J, e_y_J = plot_analyzer.plot_process_CMD(SPOTS, data_obs, band_1, band_2, band_y, age_iso, max_mag)
SPOTS_iso_J = plot_analyzer.plot_result(interval_x_J, x_J, y_J, e_x_J, e_y_J, SPOTS, band_1, band_2, band_y, data_obs, age_iso, max_mag, l=2, BTSettl=True)
plot_analyzer = PlotAnalyzer(path_all)
SPOTS = plot_analyzer.SPOTS_edr3
data_obs = data_obs_Pleiades
band_1 = 'G_abs'
band_2 = 'RP_abs'
band_y = 'G_abs'
age_iso = 0.120
max_mag = 1.4
intervals_R, interval_x_R, x_R, y_R, e_x_R, e_y_R = plot_analyzer.plot_process_CMD(SPOTS, data_obs, band_1, band_2, band_y, age_iso, max_mag)
SPOTS_iso_R = plot_analyzer.plot_result(interval_x_R, x_R, y_R, e_x_R, e_y_R, SPOTS, band_1, band_2, band_y, data_obs, age_iso, max_mag, l=2, BTSettl=True)
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6))
ax1.set_ylabel('$G$ [mag]')
ax1.set_xlabel('$G-J$ [mag]')
ax1.plot(SPOTS_edr3['00'][0.126]['G-J'], SPOTS_edr3['00'][0.126]['G_abs'], label='SPOTS-YBC f000; 126 Myr', color='k', linewidth=1, linestyle='--')
ax1.plot(SPOTS_edr3['17'][0.126]['G-J'], SPOTS_edr3['17'][0.126]['G_abs'], label='SPOTS-YBC f017; 126 Myr', color='r', linewidth=1, linestyle='--')
ax1.plot(SPOTS_edr3['34'][0.126]['G-J'], SPOTS_edr3['34'][0.126]['G_abs'], label='SPOTS-YBC f034; 126 Myr', color='orange', linewidth=1, linestyle='--')
ax1.plot(SPOTS_edr3['51'][0.126]['G-J'], SPOTS_edr3['51'][0.126]['G_abs'], label='SPOTS-YBC f051; 126 Myr', color='green', linewidth=1, linestyle='--')
ax1.plot(SPOTS_edr3['51'][0.126]['G-J'], SPOTS_edr3['51'][0.126]['G_abs'], label='SPOTS-YBC f051; 126 Myr', color='blue', linewidth=1, linestyle='--')
ax1.plot(SPOTS_edr3['85'][0.126]['G-J'], SPOTS_edr3['85'][0.126]['G_abs'], label='SPOTS-YBC f085; 126 Myr', color='magenta', linewidth=1, linestyle='--')
ax1.errorbar(data_obs_Pleiades['G_abs']-data_obs_Pleiades['J_abs'], data_obs_Pleiades['G_abs'], yerr=data_obs_Pleiades['e_g'], fmt='.', zorder=0, color='r', elinewidth=1, capsize=2, alpha=0.125)
ax1.plot(SPOTS_iso_J[0.126]['G_abs']-SPOTS_iso_J[0.126]['J_abs'], SPOTS_iso_J[0.126]['G_abs'], linewidth=1, linestyle='-', color='r', label='Mixture Isochrone G-J', zorder=5)
ax1.plot(SPOTS_iso_R[0.126]['G_abs']-SPOTS_iso_R[0.126]['J_abs'], SPOTS_iso_R[0.126]['G_abs'], linewidth=1, linestyle='-', color='b', label='Mixture Isochrone BP-RP', zorder=5)
ax1.invert_yaxis()
ax2.set_ylabel('$G$ [mag]')
ax2.set_xlabel('$B_p-R_p$ [mag]')
ax2.plot(SPOTS_edr3['00'][0.126]['BP-RP'], SPOTS_edr3['00'][0.126]['G_abs'], color='k', linewidth=1, linestyle='--')
ax2.plot(SPOTS_edr3['17'][0.126]['BP-RP'], SPOTS_edr3['17'][0.126]['G_abs'], color='r', linewidth=1, linestyle='--')
ax2.plot(SPOTS_edr3['34'][0.126]['BP-RP'], SPOTS_edr3['34'][0.126]['G_abs'], color='orange', linewidth=1, linestyle='--')
ax2.plot(SPOTS_edr3['51'][0.126]['BP-RP'], SPOTS_edr3['51'][0.126]['G_abs'], color='green', linewidth=1, linestyle='--')
ax2.plot(SPOTS_edr3['51'][0.126]['BP-RP'], SPOTS_edr3['51'][0.126]['G_abs'], color='blue', linewidth=1, linestyle='--')
ax2.plot(SPOTS_edr3['85'][0.126]['BP-RP'], SPOTS_edr3['85'][0.126]['G_abs'], color='magenta', linewidth=1, linestyle='--')
ax2.errorbar(data_obs_Pleiades['BP_abs']-data_obs_Pleiades['RP_abs'], data_obs_Pleiades['G_abs'], yerr=data_obs_Pleiades['e_g'], fmt='.', zorder=0, color='r', elinewidth=1, capsize=2, alpha=0.125)
ax2.plot(SPOTS_iso[0.126]['BP_abs']-SPOTS_iso[0.126]['RP_abs'], SPOTS_iso[0.126]['G_abs'], linewidth=1, linestyle='-', color='b', label='Mixture Isochrone BP-RP', zorder=5)
ax2.plot(SPOTS_iso_R[0.126]['BP_abs']-SPOTS_iso_R[0.126]['RP_abs'], SPOTS_iso_R[0.126]['G_abs'], linewidth=1, linestyle='-', color='r', label='Mixture Isochrone G-RP', zorder=5)
ax2.invert_yaxis()
ax2.legend(fontsize=12, loc='upper center', bbox_to_anchor=(1.35, 0.9))
for f in SPOTS_edr3.keys():
Teff_min = min(SPOTS_edr3[f][0.126]['Teff'])
print(Teff_min)
min(data_obs_Pleiades['Teff'])
min(data_obs_Pleiades['Teff_x'])
OPTION 2¶
from models_test import PlotAnalyzer
plot_analyzer = PlotAnalyzer(path_all)
SPOTS = plot_analyzer.SPOTS_edr3
data_obs = data_obs_Pleiades
age_iso = 0.120
intervals_Teff, interval_x_Teff, x_Teff, y_Teff, e_x_Teff, e_y_Teff = plot_analyzer.plot_process_HRD(SPOTS, data_obs, age_iso)
SPOTS_iso_Teff = plot_analyzer.plot_result_HRD(interval_x_Teff, x_Teff, y_Teff, e_x_Teff, e_y_Teff, SPOTS, data_obs, age_iso, l=0, BTSettl=True)
plot_analyzer = PlotAnalyzer(path_all)
age_iso = 0.120
f_FGK = '17'
f_UCDs = '34'
mid = 12
plot_analyzer.plot_HRD(SPOTS_edr3, data_obs_Pleiades, age_iso, f_FGK, f_UCDs, mid)
Relate Teff to abundances and Teff to colores.
Teff, d, BC -> M_i (correct BT-Settl to fix SPOTS for low-mass stars)
BT-Settl vs SPOTS: M, Teff¶
plt.rcParams.update({'font.size': 11, 'axes.linewidth': 1, 'axes.edgecolor': 'k'})
plt.rcParams['font.family'] = 'serif'
ages_BTSettl = list(BTSettl_Li_isochrones.keys())
ages_SPOTS = list(SPOTS_edr3['00'].keys())
SPOTS_edr3_00 = {}
BTSettl_Li_isochrones_MS = {}
for age in BTSettl_Li_isochrones.keys():
if age <= 4.0 and age >= 0.004:
closest_age = ages_SPOTS[np.abs(ages_SPOTS - age).argmin()]
SPOTS_edr3_00[closest_age] = SPOTS_edr3['00'][closest_age]
BTSettl_Li_isochrones_MS[age] = BTSettl_Li_isochrones[age]
SPOTS_edr3_full = {}
for f in SPOTS_edr3.keys():
SPOTS_edr3_full[f] = {}
ages_SPOTS = list(SPOTS_edr3[f].keys())
for age in BTSettl_Li_isochrones.keys():
if age <= 4.0 and age >= 0.004:
closest_age = ages_SPOTS[np.abs(ages_SPOTS - age).argmin()]
SPOTS_edr3_full[f][closest_age] = SPOTS_edr3[f][closest_age]
BTSettl_Li_isochrones_MS[age] = BTSettl_Li_isochrones[age]
SPOTS_edr3_full['00'].keys()
dict_keys([0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02, 0.028, 0.04, 0.05, 0.063, 0.071, 0.079, 0.089, 0.1, 0.126, 0.158, 0.2, 0.316, 0.398, 0.501, 0.631, 0.708, 0.794, 0.891, 1.0, 1.995, 3.162, 3.981])
BTSettl_Li_isochrones_MS.keys()
dict_keys([0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.12, 0.15, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 2.0, 3.0, 4.0])
from matplotlib.patches import Patch
from matplotlib.colors import Normalize, LinearSegmentedColormap
def truncate_colormap(cmap, minval=0.0, maxval=1.0, n=100):
new_cmap = LinearSegmentedColormap.from_list(
f'trunc({cmap.name},{minval},{maxval})',
cmap(np.linspace(minval, maxval, n))
)
return new_cmap
cmap_SPOTS = plt.get_cmap('Blues')
cmap_SPOTS = truncate_colormap(cmap_SPOTS, 0.25, 1)
cmap_BTSettl = plt.get_cmap('Reds')
cmap_BTSettl = truncate_colormap(cmap_BTSettl, 0.25, 1)
norm_SPOTS = plt.Normalize(vmin=np.log10(min(SPOTS_edr3_full['00'].keys()) * 1e9), vmax=np.log10(max(SPOTS_edr3_full['00'].keys()) * 1e9))
norm_BTSettl = plt.Normalize(vmin=np.log10(min(BTSettl_Li_isochrones_MS.keys()) * 1e9), vmax=np.log10(max(BTSettl_Li_isochrones_MS.keys()) * 1e9))
ages_SPOTS = [0.126, 0.079, 0.02, 0.631]
ages_BTSettl = [0.12, 0.08, 0.02, 0.6]
fig, axs = plt.subplots(1, 2, figsize=(12, 6))
for age_SPOTS in SPOTS_edr3_full['00'].keys():
log_age_SPOTS = np.log10(age_SPOTS * 1e9)
color_SPOTS = cmap_SPOTS(norm_SPOTS(log_age_SPOTS))
if age_SPOTS in ages_SPOTS:
axs[0].scatter(SPOTS_edr3_full['00'][age_SPOTS]['Teff'], SPOTS_edr3_full['00'][age_SPOTS]['Mass'], s=10, color=color_SPOTS)
for age_BTSettl in BTSettl_Li_isochrones_MS.keys():
log_age_BTSettl = np.log10(age_BTSettl * 1e9)
color_BTSettl = cmap_BTSettl(norm_BTSettl(log_age_BTSettl))
if age_BTSettl in ages_BTSettl:
axs[0].scatter(BTSettl_Li_isochrones_MS[age_BTSettl]['Teff'], BTSettl_Li_isochrones_MS[age_BTSettl]['M/Ms'], s=10, color=color_BTSettl)
cbar_SPOTS = plt.colorbar(cm.ScalarMappable(norm=norm_SPOTS, cmap=cmap_SPOTS), ax=axs[0], fraction=0.1, pad=0, orientation='horizontal', label=r'$\log{(age [yr])}$')
cbar_BTSettl = plt.colorbar(cm.ScalarMappable(norm=norm_BTSettl, cmap=cmap_BTSettl), ax=axs[0], fraction=0.1, pad=0.125, orientation='horizontal')
cbar_BTSettl.ax.set_xticklabels([])
axs[0].set_xlabel(r'$T_{eff}$')
axs[0].set_ylabel(r'$M$ [$M_{\odot}$]')
norm_SPOTS_mass = plt.Normalize(vmin=0.001, vmax=1.4)
norm_BTSettl_mass = plt.Normalize(vmin=0.001, vmax=1.4)
color_SPOTS = cmap_SPOTS(norm_SPOTS_mass(SPOTS_edr3_full['00'][0.126]['Mass']))
color_BTSettl = cmap_BTSettl(norm_BTSettl_mass(BTSettl_Li_isochrones_MS[0.12]['M/Ms']))
masses_SPOTS = np.array(SPOTS_edr3_full['00'][0.126]['Mass'])
masses_BTSettl = np.array(BTSettl_Li_isochrones_MS[0.12]['M/Ms'])
common_masses = np.intersect1d(masses_SPOTS, masses_BTSettl)
matched_SPOTS = []
matched_BTSettl = []
for mass in common_masses:
matched_SPOTS.append(mass)
matched_BTSettl.append(mass)
matched_masses = pd.DataFrame({'SPOTS': matched_SPOTS, 'BTSettl': matched_BTSettl})
mass_0 = 1.0
for mass_BTSettl in matched_masses['BTSettl']:
for age, df in BTSettl_Li_isochrones_MS.items():
masses = df['M/Ms']
if mass_BTSettl in masses.values:
index = masses[masses == mass_BTSettl].index[0]
teff_value = df['Teff'][index]
axs[1].scatter(teff_value, np.log10(age * 1e9), s=10, color=cmap_BTSettl(norm_BTSettl_mass(mass_BTSettl)))
for mass_SPOTS in matched_masses['SPOTS']:
for age, df in SPOTS_edr3_full['00'].items():
masses = df['Mass']
if mass_SPOTS in masses.values:
index = masses[masses == mass_SPOTS].index[0]
teff_value = df['Teff'][index]
axs[1].scatter(teff_value, np.log10(age * 1e9), s=10, color=cmap_SPOTS(norm_SPOTS_mass(mass_SPOTS)))
cbar_SPOTS_mass = plt.colorbar(cm.ScalarMappable(norm=norm_SPOTS_mass, cmap=cmap_SPOTS), ax=axs[1], fraction=0.1, pad=0, orientation='horizontal', label=r'$M$ [$M_{\odot}$]')
cbar_BTSettl_mass = plt.colorbar(cm.ScalarMappable(norm=norm_BTSettl_mass, cmap=cmap_BTSettl), ax=axs[1], fraction=0.1, pad=0.125, orientation='horizontal')
cbar_BTSettl_mass.ax.set_xticklabels([])
axs[1].set_xlabel(r'$T_{eff}$')
axs[1].set_ylabel(r'$\log{(age [yr])}$')
legend_elements = [
Patch(facecolor='darkred', edgecolor='darkred', label='BT-Settl'),
Patch(facecolor='darkblue', edgecolor='darkblue', label='SPOTS')
]
fig.legend(handles=legend_elements, loc='center', bbox_to_anchor=(0.5, 0.075), ncol=2)
plt.show()
SPOTS_edr3_full['00'][0.126].columns
Index(['logAge', 'Mass', 'Fspot', 'Xspot', 'log(L/Lsun)', 'log(R/Rsun)',
'log(g)', 'log(Teff)', 'log(T_hot)', 'log(T_cool)', 'TauCZ', 'Li/Li0',
'B_mag', 'V_mag', 'Rc_mag', 'Ic_mag', 'J_mag', 'H_mag', 'K_mag',
'W1_mag', 'G_mag', 'BP_mag', 'RP_mag', 'Thot', 'Tcool', 'Label_0',
'J_hot', 'H_hot', 'Ks_hot', 'Label_1', 'J_cool', 'H_cool', 'Ks_cool',
'Label_2', 'G_hot', 'G_BP_hot', 'G_RP_hot', 'Label_3', 'G_cool',
'G_BP_cool', 'G_RP_cool', 'Age_Gyr', 'BP_abs', 'RP_abs', 'G_abs',
'J_abs', 'H_abs', 'K_abs', 'M/Ms', 'BP-RP', 'G-J', 'G-RP', 'J-H', 'H-K',
'G-H', 'G-K', 'G-V', 'A(Li)', 'Lsun', 'Teff', 'M_bol'],
dtype='object')
BTSettl_Li_isochrones_MS[0.12].columns
Index(['age_Gyr', 't(Gyr)', 'M/Ms', 'Teff', 'log(L/Lsun)', 'lg(g)', 'R(Gcm)',
'D', 'Li', 'G_abs', 'BP_abs', 'RP_abs', 'J_abs', 'H_abs', 'K_abs',
'g_abs', 'r_abs', 'i_abs', 'y_abs', 'z_abs', 'A(Li)', 'Lsun'],
dtype='object')
cmap_SPOTS = plt.get_cmap('Blues')
cmap_SPOTS = truncate_colormap(cmap_SPOTS, 0.25, 1)
cmap_BTSettl = plt.get_cmap('Reds')
cmap_BTSettl = truncate_colormap(cmap_BTSettl, 0.25, 1)
fig, axs = plt.subplots(1, 2, figsize=(12, 6))
norm_SPOTS_mass = plt.Normalize(vmin=0.001, vmax=1.4)
norm_BTSettl_mass = plt.Normalize(vmin=0.001, vmax=1.4)
color_SPOTS = cmap_SPOTS(norm_SPOTS_mass(SPOTS_edr3_full['00'][0.126]['Mass']))
color_BTSettl = cmap_BTSettl(norm_BTSettl_mass(BTSettl_Li_isochrones_MS[0.12]['M/Ms']))
masses_SPOTS = np.array(SPOTS_edr3_full['00'][0.126]['Mass'])
masses_BTSettl = np.array(BTSettl_Li_isochrones_MS[0.12]['M/Ms'])
common_masses = np.intersect1d(masses_SPOTS, masses_BTSettl)
matched_SPOTS = []
matched_BTSettl = []
for mass in common_masses:
matched_SPOTS.append(mass)
matched_BTSettl.append(mass)
matched_masses = pd.DataFrame({'SPOTS': matched_SPOTS, 'BTSettl': matched_BTSettl})
mass_0 = 1.0
for mass_BTSettl in matched_masses['BTSettl']:
for age, df in BTSettl_Li_isochrones_MS.items():
masses = df['M/Ms']
if mass_BTSettl in masses.values:
index = masses[masses == mass_BTSettl].index[0]
teff_value = df['Teff'][index]
axs[0].scatter(teff_value, np.log10(age * 1e9), s=10, color=cmap_BTSettl(norm_BTSettl_mass(mass_BTSettl)))
for mass_SPOTS in matched_masses['SPOTS']:
for age, df in SPOTS_edr3_full['00'].items():
masses = df['Mass']
if mass_SPOTS in masses.values:
index = masses[masses == mass_SPOTS].index[0]
teff_value = df['Teff'][index]
axs[0].scatter(teff_value, np.log10(age * 1e9), s=10, color=cmap_SPOTS(norm_SPOTS_mass(mass_SPOTS)))
cbar_SPOTS_mass = plt.colorbar(cm.ScalarMappable(norm=norm_SPOTS_mass, cmap=cmap_SPOTS), ax=axs[0], fraction=0.1, pad=0, orientation='horizontal', label=r'$M$ [$M_{\odot}$]')
cbar_BTSettl_mass = plt.colorbar(cm.ScalarMappable(norm=norm_BTSettl_mass, cmap=cmap_BTSettl), ax=axs[0], fraction=0.1, pad=0.125, orientation='horizontal')
cbar_BTSettl_mass.ax.set_xticklabels([])
axs[0].set_xlabel(r'$T_{eff}$')
axs[0].set_ylabel(r'$\log{(age [yr])}$')
legend_elements = [
Patch(facecolor='darkred', edgecolor='darkred', label='BT-Settl'),
Patch(facecolor='darkblue', edgecolor='darkblue', label='SPOTS')
]
fig.legend(handles=legend_elements, loc='center', bbox_to_anchor=(0.5, 0.075), ncol=2)
norm_SPOTS_mass = plt.Normalize(vmin=0.001, vmax=1.4)
norm_BTSettl_mass = plt.Normalize(vmin=0.001, vmax=1.4)
color_SPOTS = cmap_SPOTS(norm_SPOTS_mass(SPOTS_edr3_full['00'][0.126]['Mass']))
color_BTSettl = cmap_BTSettl(norm_BTSettl_mass(BTSettl_Li_isochrones_MS[0.12]['M/Ms']))
masses_SPOTS = np.array(SPOTS_edr3_full['00'][0.126]['Mass'])
masses_BTSettl = np.array(BTSettl_Li_isochrones_MS[0.12]['M/Ms'])
common_masses = np.intersect1d(masses_SPOTS, masses_BTSettl)
matched_SPOTS = []
matched_BTSettl = []
for mass in common_masses:
matched_SPOTS.append(mass)
matched_BTSettl.append(mass)
matched_masses = pd.DataFrame({'SPOTS': matched_SPOTS, 'BTSettl': matched_BTSettl})
mass_0 = 1.0
for mass_BTSettl in matched_masses['BTSettl']:
for age, df in BTSettl_Li_isochrones_MS.items():
masses = df['M/Ms']
if mass_BTSettl in masses.values:
index = masses[masses == mass_BTSettl].index[0]
teff_value = df['Teff'][index]
L_value = df['Lsun'][index]
axs[1].scatter(teff_value, np.log10(L_value), s=10, color=cmap_BTSettl(norm_BTSettl_mass(mass_BTSettl)))
for mass_SPOTS in matched_masses['SPOTS']:
for age, df in SPOTS_edr3_full['00'].items():
masses = df['Mass']
if mass_SPOTS in masses.values:
index = masses[masses == mass_SPOTS].index[0]
teff_value = df['Teff'][index]
L_value = df['Lsun'][index]
axs[1].scatter(teff_value, np.log10(L_value), s=10, color=cmap_SPOTS(norm_SPOTS_mass(mass_SPOTS)))
cbar_SPOTS_mass = plt.colorbar(cm.ScalarMappable(norm=norm_SPOTS_mass, cmap=cmap_SPOTS), ax=axs[1], fraction=0.1, pad=0, orientation='horizontal', label=r'$M$ [$M_{\odot}$]')
cbar_BTSettl_mass = plt.colorbar(cm.ScalarMappable(norm=norm_BTSettl_mass, cmap=cmap_BTSettl), ax=axs[1], fraction=0.1, pad=0.125, orientation='horizontal')
cbar_BTSettl_mass.ax.set_xticklabels([])
axs[1].invert_xaxis()
axs[1].set_xlabel(r'$T_{eff}$')
axs[1].set_ylabel(r'$\log{(\mathcal{L}/\mathcal{L}_{\odot})}$')
legend_elements = [
Patch(facecolor='red', edgecolor='red', label='BT-Settl'),
Patch(facecolor='blue', edgecolor='blue', label='SPOTS')
]
fig.legend(handles=legend_elements, loc='center', bbox_to_anchor=(0.5, 0.075), ncol=2)
plt.show()
BTSettl_Li_isochrones_MS[0.12].columns
Index(['age_Gyr', 't(Gyr)', 'M/Ms', 'Teff', 'log(L/Lsun)', 'lg(g)', 'R(Gcm)',
'D', 'Li', 'G_abs', 'BP_abs', 'RP_abs', 'J_abs', 'H_abs', 'K_abs',
'g_abs', 'r_abs', 'i_abs', 'y_abs', 'z_abs', 'A(Li)', 'Lsun'],
dtype='object')
SPOTS_edr3_full['00'][0.126].columns
Index(['logAge', 'Mass', 'Fspot', 'Xspot', 'log(L/Lsun)', 'log(R/Rsun)',
'log(g)', 'log(Teff)', 'log(T_hot)', 'log(T_cool)', 'TauCZ', 'Li/Li0',
'B_mag', 'V_mag', 'Rc_mag', 'Ic_mag', 'J_mag', 'H_mag', 'K_mag',
'W1_mag', 'G_mag', 'BP_mag', 'RP_mag', 'Thot', 'Tcool', 'Label_0',
'J_hot', 'H_hot', 'Ks_hot', 'Label_1', 'J_cool', 'H_cool', 'Ks_cool',
'Label_2', 'G_hot', 'G_BP_hot', 'G_RP_hot', 'Label_3', 'G_cool',
'G_BP_cool', 'G_RP_cool', 'Age_Gyr', 'BP_abs', 'RP_abs', 'G_abs',
'J_abs', 'H_abs', 'K_abs', 'M/Ms', 'BP-RP', 'G-J', 'G-RP', 'J-H', 'H-K',
'G-H', 'G-K', 'G-V', 'A(Li)', 'Lsun', 'Teff'],
dtype='object')
mass_BTSettl_array = []
mean_Teff_array = []
mean_age_array = []
sigma_Teff_array = []
sigma_age_array = []
teff_BTSettl_values = {}
teff_SPOTS_values = {}
teff_diff_array = {}
for mass_BTSettl, mass_SPOTS in zip(matched_masses['BTSettl'], matched_masses['SPOTS']):
teff_BTSettl_values[mass_BTSettl] = []
teff_SPOTS_values[mass_SPOTS] = []
teff_diff_array[mass_BTSettl] = []
age_BTSettl_values = []
age_SPOTS_values = []
age_diff_array = []
for (age_BTSettl, df_BTSettl), (age_SPOTS, df_SPOTS) in zip(BTSettl_Li_isochrones_MS.items(), SPOTS_edr3_full['00'].items()):
masses_BTSettl = df_BTSettl['M/Ms']
masses_SPOTS = df_SPOTS['Mass']
if mass_BTSettl in masses_BTSettl.values and mass_SPOTS in masses_SPOTS.values:
index_BTSettl = masses_BTSettl[masses_BTSettl == mass_BTSettl].index[0]
teff_value_BTSettl = df_BTSettl['Teff'][index_BTSettl]
index_SPOTS = masses_SPOTS[masses_SPOTS == mass_SPOTS].index[0]
teff_value_SPOTS = df_SPOTS['Teff'][index_SPOTS]
teff_BTSettl_values[mass_BTSettl].append((teff_value_BTSettl, age_BTSettl))
teff_SPOTS_values[mass_SPOTS].append((teff_value_SPOTS, age_SPOTS))
teff_diff = np.abs(teff_value_BTSettl - teff_value_SPOTS)
teff_diff_array[mass_BTSettl].append(teff_diff)
age_value_BTSettl = age_SPOTS
age_value_SPOTS = age_BTSettl
age_BTSettl_values.append(age_value_BTSettl)
age_SPOTS_values.append(age_value_SPOTS)
age_diff = np.abs(age_value_BTSettl - age_value_SPOTS)
age_diff_array.append(age_diff)
mean_Teff = np.mean(teff_diff_array[mass_BTSettl])
mean_age = np.mean(age_diff_array)
sigma_Teff = np.std(teff_diff_array[mass_BTSettl])
sigma_age = np.std(age_diff_array)
mass_BTSettl_array.append(mass_BTSettl)
mean_Teff_array.append(mean_Teff)
mean_age_array.append(mean_age)
sigma_Teff_array.append(sigma_Teff)
sigma_age_array.append(sigma_age)
def truncate_colormap(cmap, minval=0.0, maxval=1.0, n=100):
new_cmap = plt.cm.colors.LinearSegmentedColormap.from_list(
'trunc({name},{a:.2f},{b:.2f})'.format(name=cmap.name, a=minval, b=maxval),
cmap(np.linspace(minval, maxval, n)))
return new_cmap
cmap = plt.cm.YlOrRd_r
cmap = truncate_colormap(cmap, 0, 0.65)
norm = plt.Normalize(vmin=min(mass_BTSettl_array), vmax=max(mass_BTSettl_array))
fig, ax = plt.subplots(1, 1, figsize=(6, 5))
all_ages = [age for sublist in teff_BTSettl_values.values() for (_, age) in sublist]
min_age = min(all_ages)
max_age = max(all_ages)
log_min_age = np.log10(min_age)
log_max_age = np.log10(max_age)
def get_point_size(age):
log_age = np.log10(age)
return 5 + 20 * (log_age - log_min_age) / (log_max_age - log_min_age)
for mass in mass_BTSettl_array:
teff_values = [teff for teff, age in teff_BTSettl_values[mass]]
teff_diffs = [(teff_bt - teff_sp)/teff_bt for (teff_bt, _), (teff_sp, _) in zip(teff_BTSettl_values[mass], teff_SPOTS_values[mass])]
ages = [age for teff, age in teff_BTSettl_values[mass]]
sizes = [get_point_size(age) for age in ages]
ax.scatter(teff_values, teff_diffs, s=sizes, color=cmap(norm(mass)))
ax.set_ylabel(r'$T_{eff}$ BT-Settl $-$ $T_{eff}$ SPOTS $/T_{eff}$ BT-Settl')
ax.set_xlabel(r'$T_{eff}$ BT-Settl')
ax.set_xlim(2300, 6800)
legend_ages = np.logspace(log_min_age, log_max_age, num=4)
legend_sizes = [get_point_size(age) for age in legend_ages]
for legend_age, legend_size in zip(legend_ages, legend_sizes):
ax.scatter([], [], s=legend_size, color='gray', label=f'Age {legend_age:.1e} yr')
ax.legend(loc='upper right', fontsize=8, title='Age')
sm = ScalarMappable(cmap=cmap, norm=norm)
sm.set_array([])
cbar = fig.colorbar(sm, ax=ax)
cbar.set_label(r'$M$ [$M_{\odot}$]')
cbar.ax.invert_yaxis()
plt.tight_layout()
plt.show()
from IPython.display import display, Markdown, Latex, HTML
from pylab import *
from astropy.table import Table
mean_Teff_array_rounded = [round(mean, 2) for mean in mean_Teff_array]
sigma_Teff_array_rounded = [round(sigma, 2) for sigma in sigma_Teff_array]
mean_age_array_rounded = [round(mean, 2) for mean in mean_age_array] # Convert to Myr and round
sigma_age_array_rounded = [round(sigma, 2) for sigma in sigma_age_array] # Convert to Myr and round
mass_array = [round(mass, 2) for mass in mass_BTSettl_array]
age = [round(age, 2) for age in age_BTSettl_values]
data = {
r'$M$ [$M_{\odot}$]': mass_array,
r'$\Delta\,T_{eff}$ [K]': [f'{mean} ± {sigma}' for mean, sigma in zip(mean_Teff_array_rounded, sigma_Teff_array_rounded)]}
df = pd.DataFrame(data)
latex_table = df.to_latex(index=False, column_format='|c|c|c|', escape=False)
display(HTML(df.to_html().replace('<table border="0.5" class="dataframe">', '<table border="0.5" class="dataframe" style="width:100%;">')))
| $M$ [$M_{\odot}$] | $\Delta\,T_{eff}$ [K] | |
|---|---|---|
| 0 | 0.1 | 21.91 ± 10.76 |
| 1 | 0.2 | 20.83 ± 9.13 |
| 2 | 0.3 | 27.65 ± 12.07 |
| 3 | 0.4 | 15.95 ± 7.09 |
| 4 | 0.5 | 15.14 ± 5.54 |
| 5 | 0.6 | 12.52 ± 8.3 |
| 6 | 0.7 | 13.48 ± 10.7 |
| 7 | 0.8 | 21.83 ± 13.29 |
| 8 | 0.9 | 49.34 ± 18.28 |
| 9 | 1.0 | 59.85 ± 29.1 |
| 10 | 1.1 | 56.99 ± 32.33 |
| 11 | 1.2 | 73.66 ± 32.11 |
| 12 | 1.3 | 73.95 ± 18.21 |
sigma_Teff_array
[9.74528824658613, 9.370937658478061, 12.278778443206173, 5.759022657437813, 5.056286902083065, 8.549933109049325, 10.723141222979669, 12.924107375755884, 15.443239087236211, 27.687643131857943, 32.66582299965071, 32.43308662264348, 18.46549068104321]
def find_nearest_idx(array, value):
array = np.asarray(array)
idx = (np.abs(array - value)).argmin()
return array[idx]
def inf_A_Li_models(ages, BTSettl_Li_isochrones_filtered):
trace_A_Li_models = {}
for i in range(len(ages)):
age = ages[i]
if age in BTSettl_Li_isochrones_filtered:
print(f'Isochrone age {age} Gyr is available.')
Teff = BTSettl_Li_isochrones_filtered[age]['Teff']
A_Li = BTSettl_Li_isochrones_filtered[age]['A(Li)']
else:
nearest_age = min(BTSettl_Li_isochrones_filtered.keys(), key=lambda x: abs(x - age))
print(f'Isochrone age {age} Gyr is not available; instead nearest is selected: {nearest_age} Gyr.')
Teff = BTSettl_Li_isochrones_filtered[nearest_age]['Teff']
A_Li = BTSettl_Li_isochrones_filtered[nearest_age]['A(Li)']
ages[i] = nearest_age
for age in ages:
Teff = BTSettl_Li_isochrones_filtered[age]['Teff']
A_Li = BTSettl_Li_isochrones_filtered[age]['A(Li)']
with pm.Model() as model:
T_max = pm.Uniform(r'$T_{max}$', lower=Teff.min(), upper=Teff.max())
width = pm.HalfNormal(r'$\omega$', sigma=10)
A_Li_pred = A_Li_fun(T_max, Teff, width)
#3.3 / (1 + pm.math.exp((- T_max + Teff) / width))
likelihood = pm.Normal('likelihood', mu=A_Li_pred, sigma=0.1, observed=A_Li)
trace = pm.sample(2000, tune=2000, chains=4, target_accept=0.99, nuts={'max_treedepth': 15})
trace_A_Li_models[age] = trace
return trace_A_Li_models
def A_Li_fun(T_max_mean, Teff_range, width_mean):
original_A_Li = 3.3 / (1 + np.exp((-(T_max_mean - Teff_range)) / width_mean))
A_Li = (original_A_Li / 3.3) * (3.3 + 0.7) - 0.7
return A_Li
def plot_inf_A_Li(trace_A_Li_models, BTSettl_Li_isochrones_filtered, colors):
fig, ax = plt.subplots(1, 1, figsize=(6, 5))
if len(trace_A_Li_models) != len(colors):
raise ValueError("Colors and A(Li) must have same length")
for idx, age in enumerate(trace_A_Li_models):
if age in BTSettl_Li_isochrones_filtered:
isochrone = BTSettl_Li_isochrones_filtered[age]
print(f'Isochrone age {age} Gyr is available.')
else:
closest_age = find_nearest_idx(list(BTSettl_Li_isochrones_filtered.keys()), age)
isochrone = BTSettl_Li_isochrones_filtered[closest_age]
print(f'Isochrone age {age} Gyr is not available; instead nearest is selected: {closest_age} Gyr.')
age = closest_age
T_max_mean = pm.summary(trace_A_Li_models[age])['mean'][r'$T_{max}$']
width_mean = pm.summary(trace_A_Li_models[age])['mean'][r'$\omega$']
for idx_A_Li, A_Li in isochrone['A(Li)'].items():
if A_Li == -np.inf:
A_Li_inf_idx = idx_A_Li
Teff_inf = isochrone['Teff'][A_Li_inf_idx]
isochrone = isochrone[isochrone['Teff'] < Teff_inf]
Teff = isochrone['Teff']
A_Li_values = isochrone['A(Li)']
Teff_range = np.linspace(Teff.min(), Teff.max(), 1000)
A_Li_pred_mean = A_Li_fun(T_max_mean, Teff_range, width_mean)
ax.plot(Teff_range, A_Li_pred_mean, label=f'{age} Gyr', color=colors[idx], lw=1)
ax.scatter(Teff, A_Li_values, color=colors[idx], s=10)
ax.set_xlabel(r'$T_{eff}$')
ax.set_ylabel('A(Li)')
ax.set_ylim(-0.1, 3.5)
ax.invert_xaxis()
ax.legend()
plt.show()
def generate_colors(ages):
red = [1, 0, 0]
orange = [1, 0.647, 0]
num_colors = len(ages)
colors = [np.linspace(red[i], orange[i], num_colors) for i in range(3)]
colors = np.transpose(colors)
colors = [list(color) for color in colors]
return colors
def filter_models_by_temperature(data_dict, min_temp):
filtered_data = {}
for age, df in data_dict.items():
filtered_data[age] = df[df['Teff'] < min_temp]
filtered_data[age].replace(-np.inf, 0, inplace=True)
return filtered_data
colors = generate_colors([0.02, 0.08, 0.12])
BTSettl_Li_isochrones_MS.keys()
dict_keys([0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.12, 0.15, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 2.0, 3.0, 4.0])
BTSettl_Li_isochrones_MS_filtered = filter_models_by_temperature(BTSettl_Li_isochrones_MS, 3500)
trace_A_Li_models = inf_A_Li_models([0.02, 0.08, 0.12], BTSettl_Li_isochrones_MS_filtered)
/tmp/ipykernel_1142388/3858723041.py:109: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.02 Gyr is available. Isochrone age 0.08 Gyr is available. Isochrone age 0.12 Gyr is available.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 9 seconds.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 7 seconds.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 8 seconds.
plot_inf_A_Li(trace_A_Li_models, BTSettl_Li_isochrones_MS, colors)
Isochrone age 0.02 Gyr is available. Isochrone age 0.08 Gyr is available. Isochrone age 0.12 Gyr is available.
MS_color = pd.read_csv('MS_color.csv')
for c in MS_color.columns:
if c != '#SpT':
MS_color[c] = pd.to_numeric(MS_color[c], errors='coerce')
MS_color = MS_color[10**MS_color['logT'] < 6000]
MS_color = MS_color[10**MS_color['logT'] > 1600]
MS_color['G-J'] = MS_color['G-V'] + MS_color['Mv'] - MS_color['M_J']
MS_color['G-Ks'] = MS_color['G-V'] + MS_color['Mv'] - MS_color['M_Ks']
MS_color['Teff'] = 10**MS_color['logT']
MS_color = MS_color.reset_index()
MS_color_filtered = MS_color[(MS_color['Teff'] >= 1750) & (MS_color['Teff'] <= 3000)].copy()
MS_color_filtered.loc[:, 'logR'] = np.log10(MS_color_filtered['R_Rsun'])
n = MS_color_filtered.shape[0]
MS_color_filtered
MS_color_filtered['Teff']
MS_color_filtered_ages = {}
ages = [0.02, 0.08, 0.12]
for age in ages:
MS_color_filtered_ages[age] = MS_color_filtered
T_max_mean = pm.summary(trace_A_Li_models[age])['mean'][r'$T_{max}$']
width_mean = pm.summary(trace_A_Li_models[age])['mean'][r'$\omega$']
Teff = MS_color_filtered_ages[age]['Teff']
MS_color_filtered_ages[age]['A(Li)'] = A_Li_fun(T_max_mean, Teff, width_mean)
SPOTS_expanded_A_Li_00 = {}
for age in ages:
ages_SPOTS = list(SPOTS_expanded['00'].keys())
closest_age = ages_SPOTS[np.abs(np.array(ages_SPOTS) - age).argmin()]
SPOTS_expanded_A_Li_00[age] = SPOTS_expanded['00'][closest_age].copy()
if any(value == -np.inf for value in SPOTS_expanded['00'][closest_age]['A(Li)']):
for idx_A_Li, A_Li in SPOTS_expanded_A_Li_00[age]['A(Li)'].items():
if A_Li == -np.inf and SPOTS_expanded_A_Li_00[age]['A(Li)'][idx_A_Li + 1] != -np.inf:
A_Li_inf_idx = idx_A_Li
Teff_inf = SPOTS_expanded_A_Li_00[age]['Teff'][A_Li_inf_idx]
isochrone = SPOTS_expanded_A_Li_00[age]['Teff'][SPOTS_expanded_A_Li_00[age]['Teff'] < Teff_inf]
T_max_mean = pm.summary(trace_A_Li_models[age])['mean'][r'$T_{max}$']
width_mean = pm.summary(trace_A_Li_models[age])['mean'][r'$\omega$']
for Teff in isochrone:
for Teff_idx in SPOTS_expanded_A_Li_00[age]['Teff'].index:
if Teff == SPOTS_expanded_A_Li_00[age]['Teff'][Teff_idx]:
print(age, Teff, A_Li_fun(T_max_mean, Teff, width_mean))
SPOTS_expanded_A_Li_00[age].loc[Teff_idx, 'A(Li)'] = A_Li_fun(T_max_mean, Teff, width_mean)
0.02 3372.168831901082 2.1347141515767163 0.02 3311.812416123153 3.2877814422853726 0.02 3243.5061071969367 3.299952085638701 0.02 3160.0772548060413 3.2999999451084134 0.02 3040.990896194743 3.29999999999652 0.02 2930.8932452503213 3.299999999999999 0.02 2811.9008303989394 3.3 0.02 2679.1683248190316 3.3 0.02 2570.3957827688646 3.3 0.02 2421.0290467361774 3.3 0.08 2992.504760788991 -0.17197525072124054 0.08 2930.8932452503213 2.599001517166341 0.08 2811.9008303989394 3.2988768190491236 0.08 2679.1683248190316 3.2999993092352007 0.08 2570.3957827688646 3.2999999983865322 0.08 2382.319469358689 3.2999999999999545 0.12 2930.8932452503213 -0.6970005661338959 0.12 2811.9008303989394 0.6225310888507556 0.12 2741.5741719278835 3.132472376755702 0.12 2630.2679918953813 3.2995960772846153 0.12 2421.0290467361774 3.2999999955285793 0.12 2269.8648518838213 3.2999999999988248
MS_color_filtered_ages[0.120]
| index | #SpT | Teff | logT | BCv | logL | Mbol | R_Rsun | Mv | B-V | ... | g-r | i-z | z-Y | Msun | #SpT.1 | Unnamed: 32 | G-J | G-Ks | logR | A(Li) | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 32 | 78 | M5.5V | 2930.893245 | 3.467 | -3.58 | -2.79 | 11.72 | 0.1560 | 15.30 | 1.94 | ... | 0.0 | 1.13 | 0.52 | 0.095 | NaN | NaN | 3.63 | 4.55 | -0.806875 | -0.697001 |
| 33 | 79 | M6V | 2811.900830 | 3.449 | -4.13 | -2.98 | 12.19 | 0.1370 | 16.32 | 2.01 | ... | 0.0 | 1.45 | 0.60 | 0.090 | NaN | NaN | 3.77 | 4.73 | -0.863279 | 0.622531 |
| 34 | 80 | M6.5V | 2741.574172 | 3.438 | -4.62 | -3.10 | 12.48 | 0.1260 | 17.10 | 2.07 | ... | 0.0 | 1.58 | 0.64 | 0.085 | NaN | NaN | 3.93 | 4.90 | -0.899629 | 3.132472 |
| 35 | 81 | M7V | 2679.168325 | 3.428 | -4.99 | -3.19 | 12.71 | 0.1200 | 17.70 | 2.12 | ... | 0.0 | 1.77 | 0.70 | 0.083 | NaN | NaN | 4.02 | 5.02 | -0.920819 | 3.294193 |
| 36 | 82 | M7.5V | 2630.267992 | 3.420 | -5.32 | -3.24 | 12.84 | 0.1160 | 18.16 | 2.14 | ... | 0.0 | 1.85 | 0.74 | 0.080 | NaN | NaN | 4.13 | 5.20 | -0.935542 | 3.299596 |
| 37 | 83 | M8V | 2570.395783 | 3.410 | -5.65 | -3.28 | 12.95 | 0.1140 | 18.60 | 2.15 | ... | 0.0 | 1.93 | 0.77 | 0.078 | NaN | NaN | 4.44 | 5.57 | -0.943095 | 3.299985 |
| 38 | 84 | M8.5V | 2421.029047 | 3.384 | -5.78 | -3.47 | 13.42 | 0.1040 | 19.20 | 2.16 | ... | 0.0 | 1.96 | 0.80 | 0.077 | NaN | NaN | 4.65 | 5.81 | -0.982967 | 3.300000 |
| 39 | 85 | M9V | 2382.319469 | 3.377 | -5.86 | -3.52 | 13.54 | 0.1020 | 19.40 | 2.17 | ... | 0.0 | 1.99 | 0.82 | 0.076 | NaN | NaN | 4.81 | 6.00 | -0.991400 | 3.300000 |
| 40 | 86 | M9.5V | 2349.632821 | 3.371 | -6.13 | -3.57 | 13.67 | 0.1010 | 19.75 | 0.00 | ... | 0.0 | 2.00 | 0.84 | 0.075 | NaN | NaN | 4.90 | 6.15 | -0.995679 | 3.300000 |
| 41 | 87 | L0V | 2269.864852 | 3.356 | -6.25 | -3.60 | 13.75 | 0.1020 | 20.00 | 0.00 | ... | 0.0 | 2.01 | 0.86 | 0.074 | NaN | NaN | 4.79 | 6.00 | -0.991400 | 3.300000 |
| 42 | 88 | L1V | 2157.744409 | 3.334 | -6.48 | -3.71 | 14.02 | 0.0995 | 20.50 | 0.00 | ... | 0.0 | 2.02 | 0.88 | 0.073 | NaN | NaN | 8.38 | 9.73 | -1.002177 | 3.300000 |
| 43 | 89 | L2V | 2060.629913 | 3.314 | -6.62 | -3.82 | 14.28 | 0.0970 | 20.90 | 0.00 | ... | 0.0 | 2.04 | 0.90 | 0.071 | NaN | NaN | 8.43 | 9.90 | -1.013228 | 3.300000 |
| 44 | 90 | L3V | 1918.668741 | 3.283 | -7.05 | -3.96 | 14.65 | 0.0942 | 21.70 | 0.00 | ... | 0.0 | 2.10 | 0.92 | 0.070 | NaN | NaN | 8.92 | 10.30 | -1.025949 | 3.300000 |
| 45 | 91 | L4V | 1870.682140 | 3.272 | -7.53 | -4.01 | 14.77 | 0.0940 | 22.30 | 0.00 | ... | 0.0 | 2.20 | 0.94 | 0.068 | NaN | NaN | 9.21 | 10.80 | -1.026872 | 3.300000 |
14 rows × 38 columns
SPOTS_expanded['00'][0.126]
| logAge | Mass | Fspot | Xspot | log(L/Lsun) | log(R/Rsun) | log(g) | log(Teff) | log(T_hot) | log(T_cool) | ... | G-H | G-K | G-V | A(Li) | Lsun | Teff | M_bol | BCv | RP-J | RP-H | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 8.100000 | 1.300 | 0.0 | 0.8 | 0.412322 | 0.101930 | 4.347365 | 3.813743 | 3.813743 | 0.0000 | ... | 0.94187 | 0.97556 | -0.15675 | 3.246089 | 2.584178 | 6512.426278 | 3.721184 | NaN | 0.36903 | 0.56380 |
| 1 | 8.100000 | 1.250 | 0.0 | 0.8 | 0.330458 | 0.079603 | 4.374986 | 3.804440 | 3.804440 | 0.0000 | ... | 1.00924 | 1.04499 | -0.16294 | 3.226090 | 2.140217 | 6374.414055 | 3.925846 | NaN | 0.39482 | 0.60948 |
| 2 | 8.100000 | 1.200 | 0.0 | 0.8 | 0.244443 | 0.055355 | 4.405754 | 3.795061 | 3.795061 | 0.0000 | ... | 1.07753 | 1.11557 | -0.16850 | 3.199054 | 1.755672 | 6238.221627 | 4.140882 | NaN | 0.42129 | 0.65622 |
| 3 | 8.100000 | 1.150 | 0.0 | 0.8 | 0.154305 | 0.029990 | 4.437999 | 3.785208 | 3.785208 | 0.0000 | ... | 1.14885 | 1.18969 | -0.17206 | 3.162471 | 1.426608 | 6098.292624 | 4.366229 | NaN | 0.44928 | 0.70529 |
| 4 | 8.100000 | 1.100 | 0.0 | 0.8 | 0.060162 | 0.003835 | 4.471005 | 3.774750 | 3.774750 | 0.0000 | ... | 1.22739 | 1.27197 | -0.17662 | 3.113608 | 1.148582 | 5953.197490 | 4.601585 | NaN | 0.48050 | 0.75948 |
| 5 | 8.100000 | 1.050 | 0.0 | 0.8 | -0.037916 | -0.022712 | 4.503896 | 3.763504 | 3.763504 | 0.0000 | ... | 1.31496 | 1.36354 | -0.20177 | 3.047699 | 0.916397 | 5801.018360 | 4.846781 | NaN | 0.51578 | 0.82112 |
| 6 | 8.100000 | 1.000 | 0.0 | 0.8 | -0.140140 | -0.048660 | 4.534602 | 3.750922 | 3.750922 | 0.0000 | ... | 1.40895 | 1.46179 | -0.22926 | 2.958403 | 0.724203 | 5635.369148 | 5.102339 | NaN | 0.55408 | 0.88868 |
| 7 | 8.100000 | 0.950 | 0.0 | 0.8 | -0.246858 | -0.073722 | 4.562450 | 3.736774 | 3.736774 | 0.0000 | ... | 1.52179 | 1.58106 | -0.24622 | 2.831645 | 0.566424 | 5454.735418 | 5.369136 | NaN | 0.60046 | 0.96991 |
| 8 | 8.100000 | 0.900 | 0.0 | 0.8 | -0.358181 | -0.098047 | 4.587620 | 3.721106 | 3.721106 | 0.0000 | ... | 1.65187 | 1.71735 | -0.26414 | 2.641553 | 0.438348 | 5261.455359 | 5.647442 | NaN | 0.65401 | 1.06566 |
| 9 | 8.100000 | 0.850 | 0.0 | 0.8 | -0.474711 | -0.121780 | 4.610261 | 3.703839 | 3.703839 | 0.0000 | ... | 1.80349 | 1.87674 | -0.30101 | 2.349644 | 0.335189 | 5056.377615 | 5.938767 | NaN | 0.71601 | 1.17693 |
| 10 | 8.100000 | 0.800 | 0.0 | 0.8 | -0.597804 | -0.145327 | 4.631026 | 3.684840 | 3.684840 | 0.0000 | ... | 1.97991 | 2.06267 | -0.34286 | 1.881039 | 0.252462 | 4839.938464 | 6.246499 | NaN | 0.78762 | 1.30597 |
| 11 | 8.100000 | 0.750 | 0.0 | 0.8 | -0.728511 | -0.169368 | 4.651080 | 3.664184 | 3.664184 | 0.0000 | ... | 2.19008 | 2.28384 | -0.40592 | 1.075974 | 0.186848 | 4615.127249 | 6.573268 | NaN | 0.87372 | 1.46057 |
| 12 | 8.100000 | 0.700 | 0.0 | 0.8 | -0.864977 | -0.195221 | 4.672823 | 3.642994 | 3.642994 | 0.0000 | ... | 2.40327 | 2.51537 | -0.46759 | -0.421246 | 0.136466 | 4395.352697 | 6.914432 | NaN | 0.96956 | 1.61149 |
| 13 | 8.100000 | 0.650 | 0.0 | 0.8 | -1.012628 | -0.226149 | 4.702494 | 3.621545 | 3.621545 | 0.0000 | ... | 2.57682 | 2.71848 | -0.55648 | -inf | 0.097134 | 4183.549674 | 7.283560 | NaN | 1.06201 | 1.72285 |
| 14 | 8.100000 | 0.600 | 0.0 | 0.8 | -1.152988 | -0.260413 | 4.736260 | 3.603587 | 3.603587 | 0.0000 | ... | 2.69514 | 2.86366 | -0.61492 | -inf | 0.070309 | 4014.089117 | 7.634459 | NaN | 1.13691 | 1.79490 |
| 15 | 8.100000 | 0.550 | 0.0 | 0.8 | -1.268473 | -0.291167 | 4.759979 | 3.590093 | 3.590093 | 0.0000 | ... | 2.78150 | 2.96999 | -0.68095 | -inf | 0.053892 | 3891.281506 | 7.923174 | NaN | 1.19831 | 1.84652 |
| 16 | 8.100000 | 0.500 | 0.0 | 0.8 | -1.414063 | -0.329963 | 4.796178 | 3.573093 | 3.573093 | 0.0000 | ... | 2.90841 | 3.11558 | -0.81893 | -inf | 0.038542 | 3741.906660 | 8.287148 | NaN | 1.29024 | 1.92579 |
| 17 | 8.100000 | 0.450 | 0.0 | 0.8 | -1.560148 | -0.373196 | 4.836887 | 3.558189 | 3.558189 | 0.0000 | ... | 3.03013 | 3.24735 | -0.92722 | -inf | 0.027533 | 3615.667953 | 8.652360 | NaN | 1.37750 | 2.00427 |
| 18 | 8.100000 | 0.400 | 0.0 | 0.8 | -1.690418 | -0.414652 | 4.868646 | 3.546349 | 3.546349 | 0.0000 | ... | 3.12717 | 3.34991 | -1.00765 | -inf | 0.020398 | 3518.427448 | 8.978036 | NaN | 1.44727 | 2.06791 |
| 19 | 8.100000 | 0.350 | 0.0 | 0.8 | -1.810935 | -0.454479 | 4.890308 | 3.536133 | 3.536133 | 0.0000 | ... | 3.21378 | 3.44106 | -1.10319 | -inf | 0.015455 | 3436.631053 | 9.279328 | NaN | 1.50979 | 2.12549 |
| 20 | 8.100000 | 0.300 | 0.0 | 0.8 | -1.925703 | -0.496960 | 4.908324 | 3.528682 | 3.528682 | 0.0000 | ... | 3.27957 | 3.51041 | -1.18325 | -inf | 0.011866 | 3378.171160 | 9.566247 | NaN | 1.55754 | 2.16952 |
| 21 | 8.100000 | 0.250 | 0.0 | 0.8 | -2.050513 | -0.543744 | 4.922710 | 3.520871 | 3.520871 | 0.0000 | ... | 3.35311 | 3.58805 | -1.28022 | -inf | 0.008902 | 3317.959343 | 9.878273 | NaN | 1.61108 | 2.21929 |
| 22 | 8.100000 | 0.200 | 0.0 | 0.8 | -2.201137 | -0.599432 | 4.937177 | 3.511059 | 3.511059 | 0.0000 | ... | 3.45150 | 3.69248 | -1.43388 | -inf | 0.006293 | 3243.838470 | 10.254833 | NaN | 1.68260 | 2.28711 |
| 23 | 8.100000 | 0.150 | 0.0 | 0.8 | -2.395952 | -0.669096 | 4.951565 | 3.497187 | 3.497187 | 0.0000 | ... | 3.60048 | 3.85088 | -1.73221 | -inf | 0.004018 | 3141.861887 | 10.741871 | NaN | 1.79121 | 2.39226 |
| 24 | 8.100000 | 0.100 | 0.0 | 0.8 | -2.689492 | -0.761060 | 4.959401 | 3.469784 | 3.469784 | 0.0000 | ... | 3.95060 | 4.21788 | -2.47892 | -inf | 0.002044 | 2949.742462 | 11.475720 | NaN | 2.05097 | 2.64853 |
| 25 | 8.100371 | 0.095 | 0.0 | 0.8 | -2.790000 | -0.806875 | 5.028756 | 3.467000 | 3.467000 | 2.7736 | ... | 4.21800 | 4.55000 | -1.95000 | -0.700000 | 0.001622 | 2930.893245 | 6.772189 | -3.58 | 2.25000 | 2.83800 |
| 26 | 8.100371 | 0.090 | 0.0 | 0.8 | -2.980000 | -0.863279 | 5.118083 | 3.449000 | 3.449000 | 2.7592 | ... | 4.37500 | 4.73000 | -2.37000 | 2.345323 | 0.001047 | 2811.900830 | 6.913199 | -4.13 | 2.34000 | 2.94500 |
| 27 | 8.100371 | 0.085 | 0.0 | 0.8 | -3.100000 | -0.899629 | 5.165960 | 3.438000 | 3.438000 | 2.7504 | ... | 4.53900 | 4.90000 | -2.70000 | 2.687390 | 0.000794 | 2741.574172 | 7.004074 | -4.62 | 2.45000 | 3.05900 |
| 28 | 8.100371 | 0.080 | 0.0 | 0.8 | -3.240000 | -0.935542 | 5.211456 | 3.420000 | 3.420000 | 2.7360 | ... | 4.78000 | 5.20000 | -3.15000 | 3.254725 | 0.000575 | 2630.267992 | 7.093855 | -5.32 | 2.58000 | 3.23000 |
| 29 | 8.100371 | 0.077 | 0.0 | 0.8 | -3.470000 | -0.982967 | 5.289706 | 3.384000 | 3.384000 | 2.7072 | ... | 5.34000 | 5.81000 | -3.09000 | 3.298695 | 0.000339 | 2421.029047 | 7.212417 | -5.78 | 3.06000 | 3.75000 |
| 30 | 8.100371 | 0.074 | 0.0 | 0.8 | -3.600000 | -0.991400 | 5.289313 | 3.356000 | 3.356000 | 2.6848 | ... | 5.55000 | 6.00000 | -3.45000 | 3.300000 | 0.000251 | 2269.864852 | 7.233500 | -6.25 | 3.11000 | 3.87000 |
31 rows × 64 columns
SPOTS_expanded_A_Li_00[0.12]
| logAge | Mass | Fspot | Xspot | log(L/Lsun) | log(R/Rsun) | log(g) | log(Teff) | log(T_hot) | log(T_cool) | ... | G-H | G-K | G-V | A(Li) | Lsun | Teff | M_bol | BCv | RP-J | RP-H | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 8.100000 | 1.300 | 0.0 | 0.8 | 0.412322 | 0.101930 | 4.347365 | 3.813743 | 3.813743 | 0.0000 | ... | 0.94187 | 0.97556 | -0.15675 | 3.246089 | 2.584178 | 6512.426278 | 3.721184 | NaN | 0.36903 | 0.56380 |
| 1 | 8.100000 | 1.250 | 0.0 | 0.8 | 0.330458 | 0.079603 | 4.374986 | 3.804440 | 3.804440 | 0.0000 | ... | 1.00924 | 1.04499 | -0.16294 | 3.226090 | 2.140217 | 6374.414055 | 3.925846 | NaN | 0.39482 | 0.60948 |
| 2 | 8.100000 | 1.200 | 0.0 | 0.8 | 0.244443 | 0.055355 | 4.405754 | 3.795061 | 3.795061 | 0.0000 | ... | 1.07753 | 1.11557 | -0.16850 | 3.199054 | 1.755672 | 6238.221627 | 4.140882 | NaN | 0.42129 | 0.65622 |
| 3 | 8.100000 | 1.150 | 0.0 | 0.8 | 0.154305 | 0.029990 | 4.437999 | 3.785208 | 3.785208 | 0.0000 | ... | 1.14885 | 1.18969 | -0.17206 | 3.162471 | 1.426608 | 6098.292624 | 4.366229 | NaN | 0.44928 | 0.70529 |
| 4 | 8.100000 | 1.100 | 0.0 | 0.8 | 0.060162 | 0.003835 | 4.471005 | 3.774750 | 3.774750 | 0.0000 | ... | 1.22739 | 1.27197 | -0.17662 | 3.113608 | 1.148582 | 5953.197490 | 4.601585 | NaN | 0.48050 | 0.75948 |
| 5 | 8.100000 | 1.050 | 0.0 | 0.8 | -0.037916 | -0.022712 | 4.503896 | 3.763504 | 3.763504 | 0.0000 | ... | 1.31496 | 1.36354 | -0.20177 | 3.047699 | 0.916397 | 5801.018360 | 4.846781 | NaN | 0.51578 | 0.82112 |
| 6 | 8.100000 | 1.000 | 0.0 | 0.8 | -0.140140 | -0.048660 | 4.534602 | 3.750922 | 3.750922 | 0.0000 | ... | 1.40895 | 1.46179 | -0.22926 | 2.958403 | 0.724203 | 5635.369148 | 5.102339 | NaN | 0.55408 | 0.88868 |
| 7 | 8.100000 | 0.950 | 0.0 | 0.8 | -0.246858 | -0.073722 | 4.562450 | 3.736774 | 3.736774 | 0.0000 | ... | 1.52179 | 1.58106 | -0.24622 | 2.831645 | 0.566424 | 5454.735418 | 5.369136 | NaN | 0.60046 | 0.96991 |
| 8 | 8.100000 | 0.900 | 0.0 | 0.8 | -0.358181 | -0.098047 | 4.587620 | 3.721106 | 3.721106 | 0.0000 | ... | 1.65187 | 1.71735 | -0.26414 | 2.641553 | 0.438348 | 5261.455359 | 5.647442 | NaN | 0.65401 | 1.06566 |
| 9 | 8.100000 | 0.850 | 0.0 | 0.8 | -0.474711 | -0.121780 | 4.610261 | 3.703839 | 3.703839 | 0.0000 | ... | 1.80349 | 1.87674 | -0.30101 | 2.349644 | 0.335189 | 5056.377615 | 5.938767 | NaN | 0.71601 | 1.17693 |
| 10 | 8.100000 | 0.800 | 0.0 | 0.8 | -0.597804 | -0.145327 | 4.631026 | 3.684840 | 3.684840 | 0.0000 | ... | 1.97991 | 2.06267 | -0.34286 | 1.881039 | 0.252462 | 4839.938464 | 6.246499 | NaN | 0.78762 | 1.30597 |
| 11 | 8.100000 | 0.750 | 0.0 | 0.8 | -0.728511 | -0.169368 | 4.651080 | 3.664184 | 3.664184 | 0.0000 | ... | 2.19008 | 2.28384 | -0.40592 | 1.075974 | 0.186848 | 4615.127249 | 6.573268 | NaN | 0.87372 | 1.46057 |
| 12 | 8.100000 | 0.700 | 0.0 | 0.8 | -0.864977 | -0.195221 | 4.672823 | 3.642994 | 3.642994 | 0.0000 | ... | 2.40327 | 2.51537 | -0.46759 | -0.421246 | 0.136466 | 4395.352697 | 6.914432 | NaN | 0.96956 | 1.61149 |
| 13 | 8.100000 | 0.650 | 0.0 | 0.8 | -1.012628 | -0.226149 | 4.702494 | 3.621545 | 3.621545 | 0.0000 | ... | 2.57682 | 2.71848 | -0.55648 | -inf | 0.097134 | 4183.549674 | 7.283560 | NaN | 1.06201 | 1.72285 |
| 14 | 8.100000 | 0.600 | 0.0 | 0.8 | -1.152988 | -0.260413 | 4.736260 | 3.603587 | 3.603587 | 0.0000 | ... | 2.69514 | 2.86366 | -0.61492 | -inf | 0.070309 | 4014.089117 | 7.634459 | NaN | 1.13691 | 1.79490 |
| 15 | 8.100000 | 0.550 | 0.0 | 0.8 | -1.268473 | -0.291167 | 4.759979 | 3.590093 | 3.590093 | 0.0000 | ... | 2.78150 | 2.96999 | -0.68095 | -inf | 0.053892 | 3891.281506 | 7.923174 | NaN | 1.19831 | 1.84652 |
| 16 | 8.100000 | 0.500 | 0.0 | 0.8 | -1.414063 | -0.329963 | 4.796178 | 3.573093 | 3.573093 | 0.0000 | ... | 2.90841 | 3.11558 | -0.81893 | -inf | 0.038542 | 3741.906660 | 8.287148 | NaN | 1.29024 | 1.92579 |
| 17 | 8.100000 | 0.450 | 0.0 | 0.8 | -1.560148 | -0.373196 | 4.836887 | 3.558189 | 3.558189 | 0.0000 | ... | 3.03013 | 3.24735 | -0.92722 | -inf | 0.027533 | 3615.667953 | 8.652360 | NaN | 1.37750 | 2.00427 |
| 18 | 8.100000 | 0.400 | 0.0 | 0.8 | -1.690418 | -0.414652 | 4.868646 | 3.546349 | 3.546349 | 0.0000 | ... | 3.12717 | 3.34991 | -1.00765 | -inf | 0.020398 | 3518.427448 | 8.978036 | NaN | 1.44727 | 2.06791 |
| 19 | 8.100000 | 0.350 | 0.0 | 0.8 | -1.810935 | -0.454479 | 4.890308 | 3.536133 | 3.536133 | 0.0000 | ... | 3.21378 | 3.44106 | -1.10319 | -inf | 0.015455 | 3436.631053 | 9.279328 | NaN | 1.50979 | 2.12549 |
| 20 | 8.100000 | 0.300 | 0.0 | 0.8 | -1.925703 | -0.496960 | 4.908324 | 3.528682 | 3.528682 | 0.0000 | ... | 3.27957 | 3.51041 | -1.18325 | -inf | 0.011866 | 3378.171160 | 9.566247 | NaN | 1.55754 | 2.16952 |
| 21 | 8.100000 | 0.250 | 0.0 | 0.8 | -2.050513 | -0.543744 | 4.922710 | 3.520871 | 3.520871 | 0.0000 | ... | 3.35311 | 3.58805 | -1.28022 | -inf | 0.008902 | 3317.959343 | 9.878273 | NaN | 1.61108 | 2.21929 |
| 22 | 8.100000 | 0.200 | 0.0 | 0.8 | -2.201137 | -0.599432 | 4.937177 | 3.511059 | 3.511059 | 0.0000 | ... | 3.45150 | 3.69248 | -1.43388 | -inf | 0.006293 | 3243.838470 | 10.254833 | NaN | 1.68260 | 2.28711 |
| 23 | 8.100000 | 0.150 | 0.0 | 0.8 | -2.395952 | -0.669096 | 4.951565 | 3.497187 | 3.497187 | 0.0000 | ... | 3.60048 | 3.85088 | -1.73221 | -inf | 0.004018 | 3141.861887 | 10.741871 | NaN | 1.79121 | 2.39226 |
| 24 | 8.100000 | 0.100 | 0.0 | 0.8 | -2.689492 | -0.761060 | 4.959401 | 3.469784 | 3.469784 | 0.0000 | ... | 3.95060 | 4.21788 | -2.47892 | -inf | 0.002044 | 2949.742462 | 11.475720 | NaN | 2.05097 | 2.64853 |
| 25 | 8.100371 | 0.095 | 0.0 | 0.8 | -2.790000 | -0.806875 | 5.028756 | 3.467000 | 3.467000 | 2.7736 | ... | 4.21800 | 4.55000 | -1.95000 | -0.697001 | 0.001622 | 2930.893245 | 6.772189 | -3.58 | 2.25000 | 2.83800 |
| 26 | 8.100371 | 0.090 | 0.0 | 0.8 | -2.980000 | -0.863279 | 5.118083 | 3.449000 | 3.449000 | 2.7592 | ... | 4.37500 | 4.73000 | -2.37000 | 0.622531 | 0.001047 | 2811.900830 | 6.913199 | -4.13 | 2.34000 | 2.94500 |
| 27 | 8.100371 | 0.085 | 0.0 | 0.8 | -3.100000 | -0.899629 | 5.165960 | 3.438000 | 3.438000 | 2.7504 | ... | 4.53900 | 4.90000 | -2.70000 | 3.132472 | 0.000794 | 2741.574172 | 7.004074 | -4.62 | 2.45000 | 3.05900 |
| 28 | 8.100371 | 0.080 | 0.0 | 0.8 | -3.240000 | -0.935542 | 5.211456 | 3.420000 | 3.420000 | 2.7360 | ... | 4.78000 | 5.20000 | -3.15000 | 3.299596 | 0.000575 | 2630.267992 | 7.093855 | -5.32 | 2.58000 | 3.23000 |
| 29 | 8.100371 | 0.077 | 0.0 | 0.8 | -3.470000 | -0.982967 | 5.289706 | 3.384000 | 3.384000 | 2.7072 | ... | 5.34000 | 5.81000 | -3.09000 | 3.300000 | 0.000339 | 2421.029047 | 7.212417 | -5.78 | 3.06000 | 3.75000 |
| 30 | 8.100371 | 0.074 | 0.0 | 0.8 | -3.600000 | -0.991400 | 5.289313 | 3.356000 | 3.356000 | 2.6848 | ... | 5.55000 | 6.00000 | -3.45000 | 3.300000 | 0.000251 | 2269.864852 | 7.233500 | -6.25 | 3.11000 | 3.87000 |
31 rows × 64 columns
fig, ax = plt.subplots(1, 1, figsize=(8, 6))
ax.set_ylabel('$A(Li)$ [dex]')
ax.set_xlabel('G-RP [mag]')
ax.plot(SPOTS_edr3['00'][0.126]['G_abs'] - SPOTS_edr3['00'][0.126]['RP_abs'], SPOTS_edr3['00'][0.126]['A(Li)'], linewidth=1, label='SPOTS f000')
ax.plot(SPOTS_edr3['17'][0.126]['G_abs'] - SPOTS_edr3['17'][0.126]['RP_abs'], SPOTS_edr3['00'][0.126]['A(Li)'], linewidth=1, label='SPOTS f017')
ax.plot(SPOTS_edr3['34'][0.126]['G_abs'] - SPOTS_edr3['34'][0.126]['RP_abs'], SPOTS_edr3['00'][0.126]['A(Li)'], linewidth=1, label='SPOTS f034')
#ax.scatter(SPOTS_edr3_expanded['00'][0.126]['G-RP'], SPOTS_edr3_expanded['00'][0.126]['A(Li)'], s=10, zorder=4, label='SPOTS expanded')
ax.plot(SPOTS_expanded['00'][0.126]['G-RP'], SPOTS_expanded['00'][0.126]['A(Li)'], lw=1, ls=':', color='b', zorder=4, label='SPOTS expanded')
ax.plot(SPOTS_expanded_A_Li_00[0.12]['G-RP'], SPOTS_expanded_A_Li_00[0.12]['A(Li)'], lw=1, ls=':', color='r', zorder=4, label='SPOTS expanded Esc.')
ax.set_ylim([-0.7, 3.5])
ax.plot(BTSettl_Li_isochrones[0.120]['G_abs'] - BTSettl_Li_isochrones[0.120]['RP_abs'], BTSettl_Li_isochrones[0.120]['A(Li)'], linewidth=1, label='BT-Settl', linestyle='--', color='k')
#ax.errorbar(data_obs_Pleiades['G_abs'] - data_obs_Pleiades['RP_abs'], data_obs_Pleiades['ALi'], yerr=data_obs_Pleiades['e_ALi'], fmt='.', zorder=0, color='r', elinewidth=1, capsize=2)
fig.legend(fontsize=12, loc='lower center', ncol=3, bbox_to_anchor=(0.525, -0.1))
<matplotlib.legend.Legend at 0x7fedc87e6590>
fig, ax = plt.subplots(1, 1, figsize=(8, 6))
ax.set_ylabel('$A(Li)$ [dex]')
ax.set_xlabel('G-J [mag]')
ax.plot(SPOTS_edr3['00'][0.126]['G_abs'] - SPOTS_edr3['00'][0.126]['J_abs'], SPOTS_edr3['00'][0.126]['A(Li)'], linewidth=1, label='SPOTS f000')
ax.plot(SPOTS_edr3['17'][0.126]['G_abs'] - SPOTS_edr3['17'][0.126]['J_abs'], SPOTS_edr3['00'][0.126]['A(Li)'], linewidth=1, label='SPOTS f017')
ax.plot(SPOTS_edr3['34'][0.126]['G_abs'] - SPOTS_edr3['34'][0.126]['J_abs'], SPOTS_edr3['00'][0.126]['A(Li)'], linewidth=1, label='SPOTS f034')
#ax.scatter(SPOTS_edr3_expanded['00'][0.126]['G-RP'], SPOTS_edr3_expanded['00'][0.126]['A(Li)'], s=10, zorder=4, label='SPOTS expanded')
ax.plot(SPOTS_expanded['00'][0.126]['G-J'], SPOTS_expanded['00'][0.126]['A(Li)'], lw=1, ls=':', color='b', zorder=4, label='SPOTS expanded')
ax.plot(SPOTS_expanded_A_Li_00[0.12]['G-J'], SPOTS_expanded_A_Li_00[0.12]['A(Li)'], lw=1, ls=':', color='r', zorder=4, label='SPOTS expanded Esc.')
ax.set_ylim([-0.7, 3.5])
ax.plot(BTSettl_Li_isochrones[0.120]['G_abs'] - BTSettl_Li_isochrones[0.120]['J_abs'], BTSettl_Li_isochrones[0.120]['A(Li)'], linewidth=1, label='BT-Settl', linestyle='--', color='k')
#ax.errorbar(data_obs_Pleiades['G_abs'] - data_obs_Pleiades['RP_abs'], data_obs_Pleiades['ALi'], yerr=data_obs_Pleiades['e_ALi'], fmt='.', zorder=0, color='r', elinewidth=1, capsize=2)
fig.legend(fontsize=12, loc='lower center', ncol=3, bbox_to_anchor=(0.525, -0.1))
<matplotlib.legend.Legend at 0x7fee10997310>
fig, ax = plt.subplots(1, 1, figsize=(8, 6))
ax.set_ylabel('$A(Li)$ [dex]')
ax.set_xlabel('G-J [mag]')
ax.plot(SPOTS_edr3['00'][0.126]['BP_abs'] - SPOTS_edr3['00'][0.126]['RP_abs'], SPOTS_edr3['00'][0.126]['A(Li)'], linewidth=1, label='SPOTS f000')
ax.plot(SPOTS_edr3['17'][0.126]['BP_abs'] - SPOTS_edr3['17'][0.126]['RP_abs'], SPOTS_edr3['00'][0.126]['A(Li)'], linewidth=1, label='SPOTS f017')
ax.plot(SPOTS_edr3['34'][0.126]['BP_abs'] - SPOTS_edr3['34'][0.126]['RP_abs'], SPOTS_edr3['00'][0.126]['A(Li)'], linewidth=1, label='SPOTS f034')
#ax.scatter(SPOTS_edr3_expanded['00'][0.126]['G-RP'], SPOTS_edr3_expanded['00'][0.126]['A(Li)'], s=10, zorder=4, label='SPOTS expanded')
ax.plot(SPOTS_expanded['00'][0.126]['BP-RP'][:30], SPOTS_expanded['00'][0.126]['A(Li)'][:30], lw=1, ls=':', color='b', zorder=4, label='SPOTS expanded')
ax.plot(SPOTS_expanded_A_Li_00[0.12]['BP-RP'][:30], SPOTS_expanded_A_Li_00[0.12]['A(Li)'][:30], lw=1, ls=':', color='r', zorder=4, label='SPOTS expanded Esc.')
ax.set_ylim([-0.7, 3.5])
ax.plot(BTSettl_Li_isochrones[0.120]['BP_abs'] - BTSettl_Li_isochrones[0.120]['RP_abs'], BTSettl_Li_isochrones[0.120]['A(Li)'], linewidth=1, label='BT-Settl', linestyle='--', color='k')
#ax.errorbar(data_obs_Pleiades['G_abs'] - data_obs_Pleiades['RP_abs'], data_obs_Pleiades['ALi'], yerr=data_obs_Pleiades['e_ALi'], fmt='.', zorder=0, color='r', elinewidth=1, capsize=2)
fig.legend(fontsize=12, loc='lower center', ncol=3, bbox_to_anchor=(0.525, -0.1))
<matplotlib.legend.Legend at 0x7fee0de901d0>
BP-RP do not have value in last point from UCDs
SPOTS_expanded['00'].keys()
dict_keys([0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02, 0.028, 0.04, 0.05, 0.063, 0.071, 0.079, 0.089, 0.1, 0.126, 0.158, 0.2, 0.316, 0.398, 0.501, 0.631, 0.708, 0.794, 0.891, 1.0, 1.995, 3.162])
cmap = cm.copper_r
norm = mcolors.LogNorm(vmin=min(SPOTS_expanded['00'].keys())*1e9, vmax=max(SPOTS_expanded['00'].keys())*1e9)
cmap_esc = cm.Blues
norm_esc = mcolors.LogNorm(vmin=min(SPOTS_expanded_A_Li['00'].keys())*1e9, vmax=max(SPOTS_expanded_A_Li['00'].keys())*1e9)
fig, ax = plt.subplots(1, 1, figsize=(8, 6))
ax.set_ylabel('$A(Li)$ [dex]')
ax.set_xlabel('G-RP [mag]')
for age in SPOTS_expanded['00'].keys():
age_in_years = age * 1e9
color = cmap(norm(age_in_years))
ax.plot(SPOTS_expanded['00'][age]['G-RP'], SPOTS_expanded['00'][age]['A(Li)'], lw=1, ls='-', color=color, zorder=4)
if age == 0.02:
ax.plot(SPOTS_expanded['00'][age]['G-RP'], SPOTS_expanded['00'][age]['A(Li)'], lw=1, ls='-', color='r', zorder=5, label=f'SPOTS exp. {age} Gyr')
elif age == 0.2:
ax.plot(SPOTS_expanded['00'][age]['G-RP'], SPOTS_expanded['00'][age]['A(Li)'], lw=1, ls='-', color='b', zorder=5, label=f'SPOTS exp. {age} Gyr')
for age in SPOTS_expanded['00'].keys():
if 0.02 <= age <= 0.2:
age_in_years = age * 1e9
color_esc = cmap_esc(norm_esc(age_in_years))
ax.plot(SPOTS_expanded_A_Li['00'][age]['G-RP'], SPOTS_expanded_A_Li['00'][age]['A(Li)'], lw=1, ls='-', color=color_esc, zorder=4)
ax.set_ylim([-0.2, 3.5])
ax.plot(BTSettl_Li_isochrones[0.120]['G_abs'] - BTSettl_Li_isochrones[0.120]['RP_abs'], BTSettl_Li_isochrones[0.120]['A(Li)'], linewidth=1, label='BT-Settl', linestyle='--', color='k')
sm1 = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
sm1.set_array([])
cbar1 = plt.colorbar(sm1, ax=ax, orientation='vertical', pad=0.02)
cbar1.set_label('Age [yr]')
sm2 = plt.cm.ScalarMappable(cmap=cmap_esc, norm=norm_esc)
sm2.set_array([])
cbar2 = plt.colorbar(sm2, ax=ax, orientation='vertical', pad=0.02)
cbar2.ax.set_position([cbar1.ax.get_position().x0 - 0.05, cbar1.ax.get_position().y0, cbar1.ax.get_position().width, cbar1.ax.get_position().height])
cbar2_ticks = [1e7, 5e7, 1e8, 2e8]
cbar2.set_ticks(cbar2_ticks)
cbar2.ax.set_yticklabels([f'${tick/10**int(np.log10(tick)):.0f} \\cdot 10^{{{int(np.log10(tick))}}}$' for tick in cbar2_ticks])
cbar2.ax.yaxis.set_ticks_position('left')
cbar2.ax.yaxis.set_label_position('left')
cbar2.ax.set_position([cbar1.ax.get_position().x0 - 0.045, cbar1.ax.get_position().y0, cbar1.ax.get_position().width, cbar1.ax.get_position().height])
fig.legend(fontsize=12, loc='lower center', ncol=3, bbox_to_anchor=(0.5, -0.05))
plt.show()
SPOTS extension¶
from models_test import PlotAnalyzer
plt.rcParams.update({'font.size': 11, 'axes.linewidth': 1, 'axes.edgecolor': 'k'})
plt.rcParams['font.family'] = 'serif'
SPOTS = SPOTS_edr3
f = '00'
minim_age = 0.02
maxim_age = 0.120
upplim = 0.2
plot_analyzer = PlotAnalyzer(path_all)
plot_analyzer.plot_ages(SPOTS, f, minim_age, maxim_age, upplim, BTSettl_str=True)
SPOTS = SPOTS_edr3
f = '34'
minim_age = 0.02
maxim_age = 0.120
upplim = 0.2
plot_analyzer = PlotAnalyzer(path_all)
plot_analyzer.plot_ages(SPOTS, f, minim_age, maxim_age, upplim, BTSettl_str=True)
SPOTS = SPOTS_edr3
f = '85'
minim_age = 0.02
maxim_age = 0.120
upplim = 0.2
plot_analyzer = PlotAnalyzer(path_all)
plot_analyzer.plot_ages(SPOTS, f, minim_age, maxim_age, upplim, BTSettl_str=True)
for age in SPOTS_edr3['00'].keys(): if age >= 0.08 and age < 0.2: print('____________', age) print(SPOTS_edr3['00'][age][['Teff', 'A(Li)']])
fig, ax = plt.subplots(1, 1, figsize=(8, 6))
ax.set_ylabel(r'$A(Li)$ [dex]')
ax.set_xlabel(r'$T_{eff}$ [mag]')
ax.plot(BTSettl_Li_isochrones[0.6]['Teff'], BTSettl_Li_isochrones[0.6]['A(Li)'], linewidth=1, label='600 Myr', linestyle='--', color='r')
ax.plot(BTSettl_Li_isochrones[0.7]['Teff'], BTSettl_Li_isochrones[0.7]['A(Li)'], linewidth=1, label='700 Myr', linestyle='--', color='b')
ax.plot(BTSettl_Li_isochrones[0.8]['Teff'], BTSettl_Li_isochrones[0.8]['A(Li)'], linewidth=1, label='800 Myr', linestyle='--', color='k')
ax.errorbar(data_obs_Pleiades['Teff'], data_obs_Pleiades['ALi'], yerr=data_obs_Pleiades['e_ALi'], fmt='.', zorder=0, color='r', elinewidth=1, capsize=2)
ax.invert_xaxis()
ax.legend(fontsize=12)
<matplotlib.legend.Legend at 0x7ff32dd8a710>
BTSettl ages for SPOTS¶
string = 'work'
from matplotlib.path import Path
import pymc as pm
import arviz as az
import bambi as bmb
import xarray as xr
import biosc
import biosc.preprocessing
import matplotlib.ticker as ticker
import models_test
from pymc import HalfCauchy, Model, Normal, sample
import os
import matplotlib.cm as cm
from netCDF4 import Dataset as NetCDFFile
from scipy.stats import gaussian_kde
from biosc.preprocessing import Preprocessing
from biosc.bhm import BayesianModel
import models_test
from models_test import plt, np, pd, pc, select_nearest_age
path_all = pc(string)
## to change directory:
## path_all = pc_other(string_directory)
import sys
sys.path.append(path_all)
import bmp
from bmp import BayesianModelPlots
from models_test import Models
models = Models()
from models_test import PleiadesData
path_data = path_all + 'data/Pleiades_DANCe+GDR3+2MASS+PanSTARRS1+A_Li+Lbol.csv'
pleiades_data = PleiadesData(path_data)
data_obs_Pleiades = pleiades_data.data
path_models = path_all + 'data/BT-Settl_all_Myr_Gaia+2MASS+PanSTARRS.csv'
BTSettl_mod = models.BTSettl(path_models)
BTSettl_Li_isochrones = BTSettl_mod.BTSettl_Li_isochrones
spots_instance = models.SPOTS(path_all)
SPOTS = spots_instance.SPOTS
spots_instance = models.SPOTS_YBC(path_all)
SPOTS_edr3 = spots_instance.SPOTS_edr3
from models_test import PlotAnalyzer
plt.rcParams.update({'font.size': 11, 'axes.linewidth': 1, 'axes.edgecolor': 'k'})
plt.rcParams['font.family'] = 'serif'
ages_BTSettl = list(BTSettl_Li_isochrones.keys())
ages_SPOTS = list(SPOTS_edr3['00'].keys())
SPOTS_edr3_00 = {}
BTSettl_Li_isochrones_MS = {}
for age in BTSettl_Li_isochrones.keys():
if age <= 4.0 and age >= 0.002:
closest_age = ages_SPOTS[np.abs(ages_SPOTS - age).argmin()]
SPOTS_edr3_00[closest_age] = SPOTS_edr3['00'][closest_age]
BTSettl_Li_isochrones_MS[age] = BTSettl_Li_isochrones[age]
SPOTS_edr3_full = {}
for f in SPOTS_edr3.keys():
SPOTS_edr3_full[f] = {}
ages_SPOTS = list(SPOTS_edr3[f].keys())
for age in BTSettl_Li_isochrones.keys():
if age <= 4.0 and age >= 0.002:
closest_age = ages_SPOTS[np.abs(ages_SPOTS - age).argmin()]
SPOTS_edr3_full[f][closest_age] = SPOTS_edr3[f][closest_age]
BTSettl_Li_isochrones_MS[age] = BTSettl_Li_isochrones[age]
from models_test import SPOTS_extended
from IPython.display import clear_output
BTSettl = BTSettl_Li_isochrones_MS
SPOTS = SPOTS_edr3_full
MS_color_file = 'MS_color.csv'
f = ['00']
# Must be dics!!!!
SPOTS_expanded, BTSettl_Li_isochrones_Teff_dic, SPOTS_expanded_A_Li = SPOTS_extended(MS_color_file, BTSettl, SPOTS, f).SPOTS_extension()
clear_output()
BTSettl_Li_isochrones_Teff_dic['00'].keys()
dict_keys([0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.12, 0.15, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 2.0, 3.0])
SPOTS_expanded['00'].keys()
dict_keys([0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02, 0.028, 0.04, 0.05, 0.063, 0.071, 0.079, 0.089, 0.1, 0.126, 0.158, 0.2, 0.316, 0.398, 0.501, 0.631, 0.708, 0.794, 0.891, 1.0, 1.995, 3.162])
BTSettl_Li_isochrones_Teff_dic['00'][0.4]
| index | age_Gyr | t(Gyr) | M/Ms | Teff | log(L/Lsun) | lg(g) | R(Gcm) | D | Li | ... | H_abs | K_abs | g_abs | r_abs | i_abs | y_abs | z_abs | A(Li) | Lsun | log(Teff) | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 586 | 0.4 | 0.4 | 0.100 | 2844.0 | -3.00 | 5.20 | 9.15 | 0.0 | 0.000 | ... | 9.607 | 9.321 | 16.628 | 15.657 | 13.143 | 12.113 | 11.563 | -inf | 0.001000 | 3.453930 |
| 1 | 585 | 0.4 | 0.4 | 0.090 | 2726.0 | -3.13 | 5.21 | 8.55 | 0.0 | 0.000 | ... | 9.896 | 9.605 | 17.538 | 16.519 | 13.731 | 12.544 | 11.865 | -inf | 0.000741 | 3.435526 |
| 2 | 584 | 0.4 | 0.4 | 0.080 | 2594.0 | -3.27 | 5.21 | 8.09 | 0.0 | 0.000 | ... | 10.185 | 9.892 | 18.838 | 17.466 | 14.506 | 13.080 | 12.317 | -inf | 0.000537 | 3.413970 |
| 3 | 583 | 0.4 | 0.4 | 0.075 | 2508.0 | -3.35 | 5.21 | 7.86 | 0.0 | 0.000 | ... | 10.362 | 10.069 | 19.366 | 17.858 | 14.966 | 13.401 | 12.566 | -inf | 0.000447 | 3.399328 |
| 4 | 582 | 0.4 | 0.4 | 0.072 | 2457.0 | -3.40 | 5.20 | 7.76 | 0.0 | 0.000 | ... | 10.461 | 10.170 | 19.830 | 18.079 | 15.255 | 13.596 | 12.715 | -inf | 0.000398 | 3.390405 |
| 5 | 581 | 0.4 | 0.4 | 0.070 | 2419.0 | -3.43 | 5.20 | 7.69 | 0.0 | 0.000 | ... | 10.536 | 10.247 | 20.198 | 18.244 | 15.477 | 13.744 | 12.829 | -inf | 0.000372 | 3.383636 |
| 6 | 580 | 0.4 | 0.4 | 0.050 | 1836.0 | -3.96 | 5.10 | 7.29 | 0.0 | 0.957 | ... | 11.791 | 11.211 | 22.735 | 19.825 | 17.918 | 16.173 | 15.048 | 3.280912 | 0.000110 | 3.263873 |
7 rows × 24 columns
SPOTS_expanded['00'][0.126]
| logAge | Mass | Fspot | Xspot | log(L/Lsun) | log(R/Rsun) | log(g) | log(Teff) | log(T_hot) | log(T_cool) | ... | G-H | G-K | G-V | A(Li) | Lsun | Teff | M_bol | BCv | RP-J | RP-H | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 8.100000 | 1.300 | 0.0 | 0.8 | 0.412322 | 0.101930 | 4.347365 | 3.813743 | 3.813743 | 0.0000 | ... | 0.94187 | 0.97556 | -0.15675 | 3.246089 | 2.584178 | 6512.426278 | 3.721184 | NaN | 0.36903 | 0.56380 |
| 1 | 8.100000 | 1.250 | 0.0 | 0.8 | 0.330458 | 0.079603 | 4.374986 | 3.804440 | 3.804440 | 0.0000 | ... | 1.00924 | 1.04499 | -0.16294 | 3.226090 | 2.140217 | 6374.414055 | 3.925846 | NaN | 0.39482 | 0.60948 |
| 2 | 8.100000 | 1.200 | 0.0 | 0.8 | 0.244443 | 0.055355 | 4.405754 | 3.795061 | 3.795061 | 0.0000 | ... | 1.07753 | 1.11557 | -0.16850 | 3.199054 | 1.755672 | 6238.221627 | 4.140882 | NaN | 0.42129 | 0.65622 |
| 3 | 8.100000 | 1.150 | 0.0 | 0.8 | 0.154305 | 0.029990 | 4.437999 | 3.785208 | 3.785208 | 0.0000 | ... | 1.14885 | 1.18969 | -0.17206 | 3.162471 | 1.426608 | 6098.292624 | 4.366229 | NaN | 0.44928 | 0.70529 |
| 4 | 8.100000 | 1.100 | 0.0 | 0.8 | 0.060162 | 0.003835 | 4.471005 | 3.774750 | 3.774750 | 0.0000 | ... | 1.22739 | 1.27197 | -0.17662 | 3.113608 | 1.148582 | 5953.197490 | 4.601585 | NaN | 0.48050 | 0.75948 |
| 5 | 8.100000 | 1.050 | 0.0 | 0.8 | -0.037916 | -0.022712 | 4.503896 | 3.763504 | 3.763504 | 0.0000 | ... | 1.31496 | 1.36354 | -0.20177 | 3.047699 | 0.916397 | 5801.018360 | 4.846781 | NaN | 0.51578 | 0.82112 |
| 6 | 8.100000 | 1.000 | 0.0 | 0.8 | -0.140140 | -0.048660 | 4.534602 | 3.750922 | 3.750922 | 0.0000 | ... | 1.40895 | 1.46179 | -0.22926 | 2.958403 | 0.724203 | 5635.369148 | 5.102339 | NaN | 0.55408 | 0.88868 |
| 7 | 8.100000 | 0.950 | 0.0 | 0.8 | -0.246858 | -0.073722 | 4.562450 | 3.736774 | 3.736774 | 0.0000 | ... | 1.52179 | 1.58106 | -0.24622 | 2.831645 | 0.566424 | 5454.735418 | 5.369136 | NaN | 0.60046 | 0.96991 |
| 8 | 8.100000 | 0.900 | 0.0 | 0.8 | -0.358181 | -0.098047 | 4.587620 | 3.721106 | 3.721106 | 0.0000 | ... | 1.65187 | 1.71735 | -0.26414 | 2.641553 | 0.438348 | 5261.455359 | 5.647442 | NaN | 0.65401 | 1.06566 |
| 9 | 8.100000 | 0.850 | 0.0 | 0.8 | -0.474711 | -0.121780 | 4.610261 | 3.703839 | 3.703839 | 0.0000 | ... | 1.80349 | 1.87674 | -0.30101 | 2.349644 | 0.335189 | 5056.377615 | 5.938767 | NaN | 0.71601 | 1.17693 |
| 10 | 8.100000 | 0.800 | 0.0 | 0.8 | -0.597804 | -0.145327 | 4.631026 | 3.684840 | 3.684840 | 0.0000 | ... | 1.97991 | 2.06267 | -0.34286 | 1.881039 | 0.252462 | 4839.938464 | 6.246499 | NaN | 0.78762 | 1.30597 |
| 11 | 8.100000 | 0.750 | 0.0 | 0.8 | -0.728511 | -0.169368 | 4.651080 | 3.664184 | 3.664184 | 0.0000 | ... | 2.19008 | 2.28384 | -0.40592 | 1.075974 | 0.186848 | 4615.127249 | 6.573268 | NaN | 0.87372 | 1.46057 |
| 12 | 8.100000 | 0.700 | 0.0 | 0.8 | -0.864977 | -0.195221 | 4.672823 | 3.642994 | 3.642994 | 0.0000 | ... | 2.40327 | 2.51537 | -0.46759 | -0.421246 | 0.136466 | 4395.352697 | 6.914432 | NaN | 0.96956 | 1.61149 |
| 13 | 8.100000 | 0.650 | 0.0 | 0.8 | -1.012628 | -0.226149 | 4.702494 | 3.621545 | 3.621545 | 0.0000 | ... | 2.57682 | 2.71848 | -0.55648 | -inf | 0.097134 | 4183.549674 | 7.283560 | NaN | 1.06201 | 1.72285 |
| 14 | 8.100000 | 0.600 | 0.0 | 0.8 | -1.152988 | -0.260413 | 4.736260 | 3.603587 | 3.603587 | 0.0000 | ... | 2.69514 | 2.86366 | -0.61492 | -inf | 0.070309 | 4014.089117 | 7.634459 | NaN | 1.13691 | 1.79490 |
| 15 | 8.100000 | 0.550 | 0.0 | 0.8 | -1.268473 | -0.291167 | 4.759979 | 3.590093 | 3.590093 | 0.0000 | ... | 2.78150 | 2.96999 | -0.68095 | -inf | 0.053892 | 3891.281506 | 7.923174 | NaN | 1.19831 | 1.84652 |
| 16 | 8.100000 | 0.500 | 0.0 | 0.8 | -1.414063 | -0.329963 | 4.796178 | 3.573093 | 3.573093 | 0.0000 | ... | 2.90841 | 3.11558 | -0.81893 | -inf | 0.038542 | 3741.906660 | 8.287148 | NaN | 1.29024 | 1.92579 |
| 17 | 8.100000 | 0.450 | 0.0 | 0.8 | -1.560148 | -0.373196 | 4.836887 | 3.558189 | 3.558189 | 0.0000 | ... | 3.03013 | 3.24735 | -0.92722 | -inf | 0.027533 | 3615.667953 | 8.652360 | NaN | 1.37750 | 2.00427 |
| 18 | 8.100000 | 0.400 | 0.0 | 0.8 | -1.690418 | -0.414652 | 4.868646 | 3.546349 | 3.546349 | 0.0000 | ... | 3.12717 | 3.34991 | -1.00765 | -inf | 0.020398 | 3518.427448 | 8.978036 | NaN | 1.44727 | 2.06791 |
| 19 | 8.100000 | 0.350 | 0.0 | 0.8 | -1.810935 | -0.454479 | 4.890308 | 3.536133 | 3.536133 | 0.0000 | ... | 3.21378 | 3.44106 | -1.10319 | -inf | 0.015455 | 3436.631053 | 9.279328 | NaN | 1.50979 | 2.12549 |
| 20 | 8.100000 | 0.300 | 0.0 | 0.8 | -1.925703 | -0.496960 | 4.908324 | 3.528682 | 3.528682 | 0.0000 | ... | 3.27957 | 3.51041 | -1.18325 | -inf | 0.011866 | 3378.171160 | 9.566247 | NaN | 1.55754 | 2.16952 |
| 21 | 8.100000 | 0.250 | 0.0 | 0.8 | -2.050513 | -0.543744 | 4.922710 | 3.520871 | 3.520871 | 0.0000 | ... | 3.35311 | 3.58805 | -1.28022 | -inf | 0.008902 | 3317.959343 | 9.878273 | NaN | 1.61108 | 2.21929 |
| 22 | 8.100000 | 0.200 | 0.0 | 0.8 | -2.201137 | -0.599432 | 4.937177 | 3.511059 | 3.511059 | 0.0000 | ... | 3.45150 | 3.69248 | -1.43388 | -inf | 0.006293 | 3243.838470 | 10.254833 | NaN | 1.68260 | 2.28711 |
| 23 | 8.100000 | 0.150 | 0.0 | 0.8 | -2.395952 | -0.669096 | 4.951565 | 3.497187 | 3.497187 | 0.0000 | ... | 3.60048 | 3.85088 | -1.73221 | -inf | 0.004018 | 3141.861887 | 10.741871 | NaN | 1.79121 | 2.39226 |
| 24 | 8.100000 | 0.100 | 0.0 | 0.8 | -2.689492 | -0.761060 | 4.959401 | 3.469784 | 3.469784 | 0.0000 | ... | 3.95060 | 4.21788 | -2.47892 | -inf | 0.002044 | 2949.742462 | 11.475720 | NaN | 2.05097 | 2.64853 |
| 25 | 8.100371 | 0.095 | 0.0 | 0.8 | -2.790000 | -0.806875 | 5.028756 | 3.467000 | 3.467000 | 2.7736 | ... | 4.21800 | 4.55000 | -1.95000 | -0.700000 | 0.001622 | 2930.893245 | 6.772189 | -3.58 | 2.25000 | 2.83800 |
| 26 | 8.100371 | 0.090 | 0.0 | 0.8 | -2.980000 | -0.863279 | 5.118083 | 3.449000 | 3.449000 | 2.7592 | ... | 4.37500 | 4.73000 | -2.37000 | 2.345323 | 0.001047 | 2811.900830 | 6.913199 | -4.13 | 2.34000 | 2.94500 |
| 27 | 8.100371 | 0.085 | 0.0 | 0.8 | -3.100000 | -0.899629 | 5.165960 | 3.438000 | 3.438000 | 2.7504 | ... | 4.53900 | 4.90000 | -2.70000 | 2.687390 | 0.000794 | 2741.574172 | 7.004074 | -4.62 | 2.45000 | 3.05900 |
| 28 | 8.100371 | 0.080 | 0.0 | 0.8 | -3.240000 | -0.935542 | 5.211456 | 3.420000 | 3.420000 | 2.7360 | ... | 4.78000 | 5.20000 | -3.15000 | 3.254725 | 0.000575 | 2630.267992 | 7.093855 | -5.32 | 2.58000 | 3.23000 |
| 29 | 8.100371 | 0.077 | 0.0 | 0.8 | -3.470000 | -0.982967 | 5.289706 | 3.384000 | 3.384000 | 2.7072 | ... | 5.34000 | 5.81000 | -3.09000 | 3.298695 | 0.000339 | 2421.029047 | 7.212417 | -5.78 | 3.06000 | 3.75000 |
| 30 | 8.100371 | 0.074 | 0.0 | 0.8 | -3.600000 | -0.991400 | 5.289313 | 3.356000 | 3.356000 | 2.6848 | ... | 5.55000 | 6.00000 | -3.45000 | 3.300000 | 0.000251 | 2269.864852 | 7.233500 | -6.25 | 3.11000 | 3.87000 |
31 rows × 64 columns
SPOTS_expanded_A_Li['00'][0.126]
| logAge | Mass | Fspot | Xspot | log(L/Lsun) | log(R/Rsun) | log(g) | log(Teff) | log(T_hot) | log(T_cool) | ... | G-H | G-K | G-V | A(Li) | Lsun | Teff | M_bol | BCv | RP-J | RP-H | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 8.100000 | 1.300 | 0.0 | 0.8 | 0.412322 | 0.101930 | 4.347365 | 3.813743 | 3.813743 | 0.0000 | ... | 0.94187 | 0.97556 | -0.15675 | 3.246089 | 2.584178 | 6512.426278 | 3.721184 | NaN | 0.36903 | 0.56380 |
| 1 | 8.100000 | 1.250 | 0.0 | 0.8 | 0.330458 | 0.079603 | 4.374986 | 3.804440 | 3.804440 | 0.0000 | ... | 1.00924 | 1.04499 | -0.16294 | 3.226090 | 2.140217 | 6374.414055 | 3.925846 | NaN | 0.39482 | 0.60948 |
| 2 | 8.100000 | 1.200 | 0.0 | 0.8 | 0.244443 | 0.055355 | 4.405754 | 3.795061 | 3.795061 | 0.0000 | ... | 1.07753 | 1.11557 | -0.16850 | 3.199054 | 1.755672 | 6238.221627 | 4.140882 | NaN | 0.42129 | 0.65622 |
| 3 | 8.100000 | 1.150 | 0.0 | 0.8 | 0.154305 | 0.029990 | 4.437999 | 3.785208 | 3.785208 | 0.0000 | ... | 1.14885 | 1.18969 | -0.17206 | 3.162471 | 1.426608 | 6098.292624 | 4.366229 | NaN | 0.44928 | 0.70529 |
| 4 | 8.100000 | 1.100 | 0.0 | 0.8 | 0.060162 | 0.003835 | 4.471005 | 3.774750 | 3.774750 | 0.0000 | ... | 1.22739 | 1.27197 | -0.17662 | 3.113608 | 1.148582 | 5953.197490 | 4.601585 | NaN | 0.48050 | 0.75948 |
| 5 | 8.100000 | 1.050 | 0.0 | 0.8 | -0.037916 | -0.022712 | 4.503896 | 3.763504 | 3.763504 | 0.0000 | ... | 1.31496 | 1.36354 | -0.20177 | 3.047699 | 0.916397 | 5801.018360 | 4.846781 | NaN | 0.51578 | 0.82112 |
| 6 | 8.100000 | 1.000 | 0.0 | 0.8 | -0.140140 | -0.048660 | 4.534602 | 3.750922 | 3.750922 | 0.0000 | ... | 1.40895 | 1.46179 | -0.22926 | 2.958403 | 0.724203 | 5635.369148 | 5.102339 | NaN | 0.55408 | 0.88868 |
| 7 | 8.100000 | 0.950 | 0.0 | 0.8 | -0.246858 | -0.073722 | 4.562450 | 3.736774 | 3.736774 | 0.0000 | ... | 1.52179 | 1.58106 | -0.24622 | 2.831645 | 0.566424 | 5454.735418 | 5.369136 | NaN | 0.60046 | 0.96991 |
| 8 | 8.100000 | 0.900 | 0.0 | 0.8 | -0.358181 | -0.098047 | 4.587620 | 3.721106 | 3.721106 | 0.0000 | ... | 1.65187 | 1.71735 | -0.26414 | 2.641553 | 0.438348 | 5261.455359 | 5.647442 | NaN | 0.65401 | 1.06566 |
| 9 | 8.100000 | 0.850 | 0.0 | 0.8 | -0.474711 | -0.121780 | 4.610261 | 3.703839 | 3.703839 | 0.0000 | ... | 1.80349 | 1.87674 | -0.30101 | 2.349644 | 0.335189 | 5056.377615 | 5.938767 | NaN | 0.71601 | 1.17693 |
| 10 | 8.100000 | 0.800 | 0.0 | 0.8 | -0.597804 | -0.145327 | 4.631026 | 3.684840 | 3.684840 | 0.0000 | ... | 1.97991 | 2.06267 | -0.34286 | 1.881039 | 0.252462 | 4839.938464 | 6.246499 | NaN | 0.78762 | 1.30597 |
| 11 | 8.100000 | 0.750 | 0.0 | 0.8 | -0.728511 | -0.169368 | 4.651080 | 3.664184 | 3.664184 | 0.0000 | ... | 2.19008 | 2.28384 | -0.40592 | 1.075974 | 0.186848 | 4615.127249 | 6.573268 | NaN | 0.87372 | 1.46057 |
| 12 | 8.100000 | 0.700 | 0.0 | 0.8 | -0.864977 | -0.195221 | 4.672823 | 3.642994 | 3.642994 | 0.0000 | ... | 2.40327 | 2.51537 | -0.46759 | -0.421246 | 0.136466 | 4395.352697 | 6.914432 | NaN | 0.96956 | 1.61149 |
| 13 | 8.100000 | 0.650 | 0.0 | 0.8 | -1.012628 | -0.226149 | 4.702494 | 3.621545 | 3.621545 | 0.0000 | ... | 2.57682 | 2.71848 | -0.55648 | -inf | 0.097134 | 4183.549674 | 7.283560 | NaN | 1.06201 | 1.72285 |
| 14 | 8.100000 | 0.600 | 0.0 | 0.8 | -1.152988 | -0.260413 | 4.736260 | 3.603587 | 3.603587 | 0.0000 | ... | 2.69514 | 2.86366 | -0.61492 | -inf | 0.070309 | 4014.089117 | 7.634459 | NaN | 1.13691 | 1.79490 |
| 15 | 8.100000 | 0.550 | 0.0 | 0.8 | -1.268473 | -0.291167 | 4.759979 | 3.590093 | 3.590093 | 0.0000 | ... | 2.78150 | 2.96999 | -0.68095 | -inf | 0.053892 | 3891.281506 | 7.923174 | NaN | 1.19831 | 1.84652 |
| 16 | 8.100000 | 0.500 | 0.0 | 0.8 | -1.414063 | -0.329963 | 4.796178 | 3.573093 | 3.573093 | 0.0000 | ... | 2.90841 | 3.11558 | -0.81893 | -inf | 0.038542 | 3741.906660 | 8.287148 | NaN | 1.29024 | 1.92579 |
| 17 | 8.100000 | 0.450 | 0.0 | 0.8 | -1.560148 | -0.373196 | 4.836887 | 3.558189 | 3.558189 | 0.0000 | ... | 3.03013 | 3.24735 | -0.92722 | -inf | 0.027533 | 3615.667953 | 8.652360 | NaN | 1.37750 | 2.00427 |
| 18 | 8.100000 | 0.400 | 0.0 | 0.8 | -1.690418 | -0.414652 | 4.868646 | 3.546349 | 3.546349 | 0.0000 | ... | 3.12717 | 3.34991 | -1.00765 | -inf | 0.020398 | 3518.427448 | 8.978036 | NaN | 1.44727 | 2.06791 |
| 19 | 8.100000 | 0.350 | 0.0 | 0.8 | -1.810935 | -0.454479 | 4.890308 | 3.536133 | 3.536133 | 0.0000 | ... | 3.21378 | 3.44106 | -1.10319 | -inf | 0.015455 | 3436.631053 | 9.279328 | NaN | 1.50979 | 2.12549 |
| 20 | 8.100000 | 0.300 | 0.0 | 0.8 | -1.925703 | -0.496960 | 4.908324 | 3.528682 | 3.528682 | 0.0000 | ... | 3.27957 | 3.51041 | -1.18325 | -inf | 0.011866 | 3378.171160 | 9.566247 | NaN | 1.55754 | 2.16952 |
| 21 | 8.100000 | 0.250 | 0.0 | 0.8 | -2.050513 | -0.543744 | 4.922710 | 3.520871 | 3.520871 | 0.0000 | ... | 3.35311 | 3.58805 | -1.28022 | -inf | 0.008902 | 3317.959343 | 9.878273 | NaN | 1.61108 | 2.21929 |
| 22 | 8.100000 | 0.200 | 0.0 | 0.8 | -2.201137 | -0.599432 | 4.937177 | 3.511059 | 3.511059 | 0.0000 | ... | 3.45150 | 3.69248 | -1.43388 | -inf | 0.006293 | 3243.838470 | 10.254833 | NaN | 1.68260 | 2.28711 |
| 23 | 8.100000 | 0.150 | 0.0 | 0.8 | -2.395952 | -0.669096 | 4.951565 | 3.497187 | 3.497187 | 0.0000 | ... | 3.60048 | 3.85088 | -1.73221 | -inf | 0.004018 | 3141.861887 | 10.741871 | NaN | 1.79121 | 2.39226 |
| 24 | 8.100000 | 0.100 | 0.0 | 0.8 | -2.689492 | -0.761060 | 4.959401 | 3.469784 | 3.469784 | 0.0000 | ... | 3.95060 | 4.21788 | -2.47892 | -inf | 0.002044 | 2949.742462 | 11.475720 | NaN | 2.05097 | 2.64853 |
| 25 | 8.100371 | 0.095 | 0.0 | 0.8 | -2.790000 | -0.806875 | 5.028756 | 3.467000 | 3.467000 | 2.7736 | ... | 4.21800 | 4.55000 | -1.95000 | -0.696962 | 0.001622 | 2930.893245 | 6.772189 | -3.58 | 2.25000 | 2.83800 |
| 26 | 8.100371 | 0.090 | 0.0 | 0.8 | -2.980000 | -0.863279 | 5.118083 | 3.449000 | 3.449000 | 2.7592 | ... | 4.37500 | 4.73000 | -2.37000 | 0.624019 | 0.001047 | 2811.900830 | 6.913199 | -4.13 | 2.34000 | 2.94500 |
| 27 | 8.100371 | 0.085 | 0.0 | 0.8 | -3.100000 | -0.899629 | 5.165960 | 3.438000 | 3.438000 | 2.7504 | ... | 4.53900 | 4.90000 | -2.70000 | 3.131701 | 0.000794 | 2741.574172 | 7.004074 | -4.62 | 2.45000 | 3.05900 |
| 28 | 8.100371 | 0.080 | 0.0 | 0.8 | -3.240000 | -0.935542 | 5.211456 | 3.420000 | 3.420000 | 2.7360 | ... | 4.78000 | 5.20000 | -3.15000 | 3.299590 | 0.000575 | 2630.267992 | 7.093855 | -5.32 | 2.58000 | 3.23000 |
| 29 | 8.100371 | 0.077 | 0.0 | 0.8 | -3.470000 | -0.982967 | 5.289706 | 3.384000 | 3.384000 | 2.7072 | ... | 5.34000 | 5.81000 | -3.09000 | 3.300000 | 0.000339 | 2421.029047 | 7.212417 | -5.78 | 3.06000 | 3.75000 |
| 30 | 8.100371 | 0.074 | 0.0 | 0.8 | -3.600000 | -0.991400 | 5.289313 | 3.356000 | 3.356000 | 2.6848 | ... | 5.55000 | 6.00000 | -3.45000 | 3.300000 | 0.000251 | 2269.864852 | 7.233500 | -6.25 | 3.11000 | 3.87000 |
31 rows × 64 columns
ages = [0.01, 0.02, 0.08, 0.120, 0.150, 0.400]
SPOTS_extended(MS_color_file, BTSettl, SPOTS, f).plot_CMD_ALi(SPOTS_expanded, SPOTS_expanded_A_Li, BTSettl_Li_isochrones_Teff_dic, BTSettl_Li_isochrones, data_obs_Pleiades, ages)
from reportlab.lib.pagesizes import letter
from reportlab.pdfgen import canvas
from reportlab.lib.utils import ImageReader
def add_images_to_pdf(image1_path, image2_path, image3_path, output_pdf_path):
if not os.path.exists(image1_path):
print(f"Error: {image1_path} does not exist.")
return
if not os.path.exists(image2_path):
print(f"Error: {image2_path} does not exist.")
return
if not os.path.exists(image3_path):
print(f"Error: {image3_path} does not exist.")
return
try:
c = canvas.Canvas(output_pdf_path, pagesize=letter)
width, height = letter
img1 = ImageReader(image1_path)
img2 = ImageReader(image2_path)
img3 = ImageReader(image3_path)
img1_width, img1_height = img1.getSize()
img2_width, img2_height = img2.getSize()
img3_width, img3_height = img3.getSize()
max_img3_width = width * 0.8
scale_factor3 = min(max_img3_width / img3_width, 1.0)
scaled_img3_width = img3_width * scale_factor3
scaled_img3_height = img3_height * scale_factor3
img3_x = (width - scaled_img3_width) / 2
img3_y = 0
c.drawImage(image1_path, 0, height * 2 / 3, width=width, height=height / 3)
c.drawImage(image2_path, 0, height * 1 / 3, width=width, height=height / 3)
c.drawImage(image3_path, img3_x, img3_y, width=scaled_img3_width, height=scaled_img3_height)
c.save()
print(f"PDF saved: {output_pdf_path}")
except Exception as e:
print(f"Error: {e}")
image1_path = "/pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/017-Notion/0171-1.5/A(Li)_SPOTS_00.png"
image2_path = "/pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/017-Notion/0171-1.5/CMD_SPOTS_00.png"
image3_path = "/pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/017-Notion/0171-1.5/Mass_vs_Teff_vs_Age_SPOTSvsBTSettl.png"
output_pdf_path = "/pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/017-Notion/0171-1.5/extended.pdf"
add_images_to_pdf(image1_path, image2_path, image3_path, output_pdf_path)
PDF saved: /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/017-Notion/0171-1.5/extended.pdf
SPOTS_expanded['00'][0.126]
| logAge | Mass | Fspot | Xspot | log(L/Lsun) | log(R/Rsun) | log(g) | log(Teff) | log(T_hot) | log(T_cool) | ... | G-H | G-K | G-V | A(Li) | Lsun | Teff | M_bol | BCv | RP-J | RP-H | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 8.100000 | 1.300 | 0.0 | 0.8 | 0.412322 | 0.101930 | 4.347365 | 3.813743 | 3.813743 | 0.0000 | ... | 0.94187 | 0.97556 | -0.15675 | 3.246089 | 2.584178 | 6512.426278 | 3.721184 | NaN | 0.36903 | 0.56380 |
| 1 | 8.100000 | 1.250 | 0.0 | 0.8 | 0.330458 | 0.079603 | 4.374986 | 3.804440 | 3.804440 | 0.0000 | ... | 1.00924 | 1.04499 | -0.16294 | 3.226090 | 2.140217 | 6374.414055 | 3.925846 | NaN | 0.39482 | 0.60948 |
| 2 | 8.100000 | 1.200 | 0.0 | 0.8 | 0.244443 | 0.055355 | 4.405754 | 3.795061 | 3.795061 | 0.0000 | ... | 1.07753 | 1.11557 | -0.16850 | 3.199054 | 1.755672 | 6238.221627 | 4.140882 | NaN | 0.42129 | 0.65622 |
| 3 | 8.100000 | 1.150 | 0.0 | 0.8 | 0.154305 | 0.029990 | 4.437999 | 3.785208 | 3.785208 | 0.0000 | ... | 1.14885 | 1.18969 | -0.17206 | 3.162471 | 1.426608 | 6098.292624 | 4.366229 | NaN | 0.44928 | 0.70529 |
| 4 | 8.100000 | 1.100 | 0.0 | 0.8 | 0.060162 | 0.003835 | 4.471005 | 3.774750 | 3.774750 | 0.0000 | ... | 1.22739 | 1.27197 | -0.17662 | 3.113608 | 1.148582 | 5953.197490 | 4.601585 | NaN | 0.48050 | 0.75948 |
| 5 | 8.100000 | 1.050 | 0.0 | 0.8 | -0.037916 | -0.022712 | 4.503896 | 3.763504 | 3.763504 | 0.0000 | ... | 1.31496 | 1.36354 | -0.20177 | 3.047699 | 0.916397 | 5801.018360 | 4.846781 | NaN | 0.51578 | 0.82112 |
| 6 | 8.100000 | 1.000 | 0.0 | 0.8 | -0.140140 | -0.048660 | 4.534602 | 3.750922 | 3.750922 | 0.0000 | ... | 1.40895 | 1.46179 | -0.22926 | 2.958403 | 0.724203 | 5635.369148 | 5.102339 | NaN | 0.55408 | 0.88868 |
| 7 | 8.100000 | 0.950 | 0.0 | 0.8 | -0.246858 | -0.073722 | 4.562450 | 3.736774 | 3.736774 | 0.0000 | ... | 1.52179 | 1.58106 | -0.24622 | 2.831645 | 0.566424 | 5454.735418 | 5.369136 | NaN | 0.60046 | 0.96991 |
| 8 | 8.100000 | 0.900 | 0.0 | 0.8 | -0.358181 | -0.098047 | 4.587620 | 3.721106 | 3.721106 | 0.0000 | ... | 1.65187 | 1.71735 | -0.26414 | 2.641553 | 0.438348 | 5261.455359 | 5.647442 | NaN | 0.65401 | 1.06566 |
| 9 | 8.100000 | 0.850 | 0.0 | 0.8 | -0.474711 | -0.121780 | 4.610261 | 3.703839 | 3.703839 | 0.0000 | ... | 1.80349 | 1.87674 | -0.30101 | 2.349644 | 0.335189 | 5056.377615 | 5.938767 | NaN | 0.71601 | 1.17693 |
| 10 | 8.100000 | 0.800 | 0.0 | 0.8 | -0.597804 | -0.145327 | 4.631026 | 3.684840 | 3.684840 | 0.0000 | ... | 1.97991 | 2.06267 | -0.34286 | 1.881039 | 0.252462 | 4839.938464 | 6.246499 | NaN | 0.78762 | 1.30597 |
| 11 | 8.100000 | 0.750 | 0.0 | 0.8 | -0.728511 | -0.169368 | 4.651080 | 3.664184 | 3.664184 | 0.0000 | ... | 2.19008 | 2.28384 | -0.40592 | 1.075974 | 0.186848 | 4615.127249 | 6.573268 | NaN | 0.87372 | 1.46057 |
| 12 | 8.100000 | 0.700 | 0.0 | 0.8 | -0.864977 | -0.195221 | 4.672823 | 3.642994 | 3.642994 | 0.0000 | ... | 2.40327 | 2.51537 | -0.46759 | -0.421246 | 0.136466 | 4395.352697 | 6.914432 | NaN | 0.96956 | 1.61149 |
| 13 | 8.100000 | 0.650 | 0.0 | 0.8 | -1.012628 | -0.226149 | 4.702494 | 3.621545 | 3.621545 | 0.0000 | ... | 2.57682 | 2.71848 | -0.55648 | -inf | 0.097134 | 4183.549674 | 7.283560 | NaN | 1.06201 | 1.72285 |
| 14 | 8.100000 | 0.600 | 0.0 | 0.8 | -1.152988 | -0.260413 | 4.736260 | 3.603587 | 3.603587 | 0.0000 | ... | 2.69514 | 2.86366 | -0.61492 | -inf | 0.070309 | 4014.089117 | 7.634459 | NaN | 1.13691 | 1.79490 |
| 15 | 8.100000 | 0.550 | 0.0 | 0.8 | -1.268473 | -0.291167 | 4.759979 | 3.590093 | 3.590093 | 0.0000 | ... | 2.78150 | 2.96999 | -0.68095 | -inf | 0.053892 | 3891.281506 | 7.923174 | NaN | 1.19831 | 1.84652 |
| 16 | 8.100000 | 0.500 | 0.0 | 0.8 | -1.414063 | -0.329963 | 4.796178 | 3.573093 | 3.573093 | 0.0000 | ... | 2.90841 | 3.11558 | -0.81893 | -inf | 0.038542 | 3741.906660 | 8.287148 | NaN | 1.29024 | 1.92579 |
| 17 | 8.100000 | 0.450 | 0.0 | 0.8 | -1.560148 | -0.373196 | 4.836887 | 3.558189 | 3.558189 | 0.0000 | ... | 3.03013 | 3.24735 | -0.92722 | -inf | 0.027533 | 3615.667953 | 8.652360 | NaN | 1.37750 | 2.00427 |
| 18 | 8.100000 | 0.400 | 0.0 | 0.8 | -1.690418 | -0.414652 | 4.868646 | 3.546349 | 3.546349 | 0.0000 | ... | 3.12717 | 3.34991 | -1.00765 | -inf | 0.020398 | 3518.427448 | 8.978036 | NaN | 1.44727 | 2.06791 |
| 19 | 8.100000 | 0.350 | 0.0 | 0.8 | -1.810935 | -0.454479 | 4.890308 | 3.536133 | 3.536133 | 0.0000 | ... | 3.21378 | 3.44106 | -1.10319 | -inf | 0.015455 | 3436.631053 | 9.279328 | NaN | 1.50979 | 2.12549 |
| 20 | 8.100000 | 0.300 | 0.0 | 0.8 | -1.925703 | -0.496960 | 4.908324 | 3.528682 | 3.528682 | 0.0000 | ... | 3.27957 | 3.51041 | -1.18325 | -inf | 0.011866 | 3378.171160 | 9.566247 | NaN | 1.55754 | 2.16952 |
| 21 | 8.100000 | 0.250 | 0.0 | 0.8 | -2.050513 | -0.543744 | 4.922710 | 3.520871 | 3.520871 | 0.0000 | ... | 3.35311 | 3.58805 | -1.28022 | -inf | 0.008902 | 3317.959343 | 9.878273 | NaN | 1.61108 | 2.21929 |
| 22 | 8.100000 | 0.200 | 0.0 | 0.8 | -2.201137 | -0.599432 | 4.937177 | 3.511059 | 3.511059 | 0.0000 | ... | 3.45150 | 3.69248 | -1.43388 | -inf | 0.006293 | 3243.838470 | 10.254833 | NaN | 1.68260 | 2.28711 |
| 23 | 8.100000 | 0.150 | 0.0 | 0.8 | -2.395952 | -0.669096 | 4.951565 | 3.497187 | 3.497187 | 0.0000 | ... | 3.60048 | 3.85088 | -1.73221 | -inf | 0.004018 | 3141.861887 | 10.741871 | NaN | 1.79121 | 2.39226 |
| 24 | 8.100000 | 0.100 | 0.0 | 0.8 | -2.689492 | -0.761060 | 4.959401 | 3.469784 | 3.469784 | 0.0000 | ... | 3.95060 | 4.21788 | -2.47892 | -inf | 0.002044 | 2949.742462 | 11.475720 | NaN | 2.05097 | 2.64853 |
| 25 | 8.100371 | 0.095 | 0.0 | 0.8 | -2.790000 | -0.806875 | 5.028756 | 3.467000 | 3.467000 | 2.7736 | ... | 4.21800 | 4.55000 | -1.95000 | -0.700000 | 0.001622 | 2930.893245 | 6.772189 | -3.58 | 2.25000 | 2.83800 |
| 26 | 8.100371 | 0.090 | 0.0 | 0.8 | -2.980000 | -0.863279 | 5.118083 | 3.449000 | 3.449000 | 2.7592 | ... | 4.37500 | 4.73000 | -2.37000 | 2.345323 | 0.001047 | 2811.900830 | 6.913199 | -4.13 | 2.34000 | 2.94500 |
| 27 | 8.100371 | 0.085 | 0.0 | 0.8 | -3.100000 | -0.899629 | 5.165960 | 3.438000 | 3.438000 | 2.7504 | ... | 4.53900 | 4.90000 | -2.70000 | 2.687390 | 0.000794 | 2741.574172 | 7.004074 | -4.62 | 2.45000 | 3.05900 |
| 28 | 8.100371 | 0.080 | 0.0 | 0.8 | -3.240000 | -0.935542 | 5.211456 | 3.420000 | 3.420000 | 2.7360 | ... | 4.78000 | 5.20000 | -3.15000 | 3.254725 | 0.000575 | 2630.267992 | 7.093855 | -5.32 | 2.58000 | 3.23000 |
| 29 | 8.100371 | 0.077 | 0.0 | 0.8 | -3.470000 | -0.982967 | 5.289706 | 3.384000 | 3.384000 | 2.7072 | ... | 5.34000 | 5.81000 | -3.09000 | 3.298695 | 0.000339 | 2421.029047 | 7.212417 | -5.78 | 3.06000 | 3.75000 |
| 30 | 8.100371 | 0.074 | 0.0 | 0.8 | -3.600000 | -0.991400 | 5.289313 | 3.356000 | 3.356000 | 2.6848 | ... | 5.55000 | 6.00000 | -3.45000 | 3.300000 | 0.000251 | 2269.864852 | 7.233500 | -6.25 | 3.11000 | 3.87000 |
31 rows × 64 columns
from models_test import SPOTS_extended
BTSettl = BTSettl_Li_isochrones_MS
SPOTS = SPOTS_edr3_full
MS_color_file = 'MS_color.csv'
f = ['00', '17', '34', '51', '68', '85']
# Must be dics!!!! ^^^^
SPOTS_expanded_full, BTSettl_Li_isochrones_Teff_dic, SPOTS_expanded_A_Li = SPOTS_extended(MS_color_file, BTSettl, SPOTS, f).SPOTS_extension()
/pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.02 Gyr is available.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 9 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.028 Gyr is not available; instead nearest is selected: 0.03 Gyr.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 8 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.04 Gyr is available.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 7 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.05 Gyr is available.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 6 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.063 Gyr is not available; instead nearest is selected: 0.06 Gyr.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 6 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.071 Gyr is not available; instead nearest is selected: 0.07 Gyr.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 5 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.079 Gyr is not available; instead nearest is selected: 0.08 Gyr.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 9 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.089 Gyr is not available; instead nearest is selected: 0.09 Gyr.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 13 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.1 Gyr is available.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 6 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.126 Gyr is not available; instead nearest is selected: 0.12 Gyr.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 6 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.158 Gyr is not available; instead nearest is selected: 0.15 Gyr.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 6 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.2 Gyr is available.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 6 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.02 Gyr is available.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 9 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.028 Gyr is not available; instead nearest is selected: 0.03 Gyr.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 9 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.04 Gyr is available.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 7 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.05 Gyr is available.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 7 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.063 Gyr is not available; instead nearest is selected: 0.06 Gyr.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 7 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.071 Gyr is not available; instead nearest is selected: 0.07 Gyr.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 5 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.079 Gyr is not available; instead nearest is selected: 0.08 Gyr.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 8 seconds. The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details The effective sample size per chain is smaller than 100 for some parameters. A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details There were 16 divergences after tuning. Increase `target_accept` or reparameterize. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.089 Gyr is not available; instead nearest is selected: 0.09 Gyr.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 12 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.1 Gyr is available.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 6 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.126 Gyr is not available; instead nearest is selected: 0.12 Gyr.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 6 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.158 Gyr is not available; instead nearest is selected: 0.15 Gyr.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 6 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.2 Gyr is available.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 16 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.028 Gyr is not available; instead nearest is selected: 0.03 Gyr.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 7 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.04 Gyr is available.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 7 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.05 Gyr is available.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 6 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.063 Gyr is not available; instead nearest is selected: 0.06 Gyr.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 7 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.071 Gyr is not available; instead nearest is selected: 0.07 Gyr.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 6 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.079 Gyr is not available; instead nearest is selected: 0.08 Gyr.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 6 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.089 Gyr is not available; instead nearest is selected: 0.09 Gyr.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 12 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.1 Gyr is available.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 6 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.126 Gyr is not available; instead nearest is selected: 0.12 Gyr.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 6 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.158 Gyr is not available; instead nearest is selected: 0.15 Gyr.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 8 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.2 Gyr is available.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 6 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.028 Gyr is not available; instead nearest is selected: 0.03 Gyr.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 7 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.04 Gyr is available.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 7 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.05 Gyr is available.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 6 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.063 Gyr is not available; instead nearest is selected: 0.06 Gyr.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 6 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.071 Gyr is not available; instead nearest is selected: 0.07 Gyr.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 6 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.079 Gyr is not available; instead nearest is selected: 0.08 Gyr.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 8 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.089 Gyr is not available; instead nearest is selected: 0.09 Gyr.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 7 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.1 Gyr is available.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 6 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.126 Gyr is not available; instead nearest is selected: 0.12 Gyr.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 6 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.158 Gyr is not available; instead nearest is selected: 0.15 Gyr.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 7 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.2 Gyr is available.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 6 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.04 Gyr is available.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 6 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.05 Gyr is available.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 6 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.063 Gyr is not available; instead nearest is selected: 0.06 Gyr.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 7 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.071 Gyr is not available; instead nearest is selected: 0.07 Gyr.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 6 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.079 Gyr is not available; instead nearest is selected: 0.08 Gyr.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 6 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.089 Gyr is not available; instead nearest is selected: 0.09 Gyr.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 7 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.1 Gyr is available.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 6 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.126 Gyr is not available; instead nearest is selected: 0.12 Gyr.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 7 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.158 Gyr is not available; instead nearest is selected: 0.15 Gyr.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 7 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.2 Gyr is available.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 6 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.04 Gyr is available.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 6 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.05 Gyr is available.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 6 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.063 Gyr is not available; instead nearest is selected: 0.06 Gyr.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 7 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.071 Gyr is not available; instead nearest is selected: 0.07 Gyr.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 6 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.079 Gyr is not available; instead nearest is selected: 0.08 Gyr.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 6 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.089 Gyr is not available; instead nearest is selected: 0.09 Gyr.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 6 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.1 Gyr is available.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 6 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.126 Gyr is not available; instead nearest is selected: 0.12 Gyr.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 6 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.158 Gyr is not available; instead nearest is selected: 0.15 Gyr.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 6 seconds. /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/models_test.py:1413: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy filtered_data[age].replace(-np.inf, 0, inplace=True)
Isochrone age 0.2 Gyr is available.
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [$T_{max}$, $\omega$]
Sampling 4 chains for 2_000 tune and 2_000 draw iterations (8_000 + 8_000 draws total) took 25 seconds. There were 7 divergences after tuning. Increase `target_accept` or reparameterize.
SPOTS_expanded_A_Li['00'][0.126]
| logAge | Mass | Fspot | Xspot | log(L/Lsun) | log(R/Rsun) | log(g) | log(Teff) | log(T_hot) | log(T_cool) | ... | G-H | G-K | G-V | A(Li) | Lsun | Teff | M_bol | BCv | RP-J | RP-H | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 8.100000 | 1.300 | 0.0 | 0.8 | 0.412322 | 0.101930 | 4.347365 | 3.813743 | 3.813743 | 0.0000 | ... | 0.94187 | 0.97556 | -0.15675 | 3.246089 | 2.584178 | 6512.426278 | 3.721184 | NaN | 0.36903 | 0.56380 |
| 1 | 8.100000 | 1.250 | 0.0 | 0.8 | 0.330458 | 0.079603 | 4.374986 | 3.804440 | 3.804440 | 0.0000 | ... | 1.00924 | 1.04499 | -0.16294 | 3.226090 | 2.140217 | 6374.414055 | 3.925846 | NaN | 0.39482 | 0.60948 |
| 2 | 8.100000 | 1.200 | 0.0 | 0.8 | 0.244443 | 0.055355 | 4.405754 | 3.795061 | 3.795061 | 0.0000 | ... | 1.07753 | 1.11557 | -0.16850 | 3.199054 | 1.755672 | 6238.221627 | 4.140882 | NaN | 0.42129 | 0.65622 |
| 3 | 8.100000 | 1.150 | 0.0 | 0.8 | 0.154305 | 0.029990 | 4.437999 | 3.785208 | 3.785208 | 0.0000 | ... | 1.14885 | 1.18969 | -0.17206 | 3.162471 | 1.426608 | 6098.292624 | 4.366229 | NaN | 0.44928 | 0.70529 |
| 4 | 8.100000 | 1.100 | 0.0 | 0.8 | 0.060162 | 0.003835 | 4.471005 | 3.774750 | 3.774750 | 0.0000 | ... | 1.22739 | 1.27197 | -0.17662 | 3.113608 | 1.148582 | 5953.197490 | 4.601585 | NaN | 0.48050 | 0.75948 |
| 5 | 8.100000 | 1.050 | 0.0 | 0.8 | -0.037916 | -0.022712 | 4.503896 | 3.763504 | 3.763504 | 0.0000 | ... | 1.31496 | 1.36354 | -0.20177 | 3.047699 | 0.916397 | 5801.018360 | 4.846781 | NaN | 0.51578 | 0.82112 |
| 6 | 8.100000 | 1.000 | 0.0 | 0.8 | -0.140140 | -0.048660 | 4.534602 | 3.750922 | 3.750922 | 0.0000 | ... | 1.40895 | 1.46179 | -0.22926 | 2.958403 | 0.724203 | 5635.369148 | 5.102339 | NaN | 0.55408 | 0.88868 |
| 7 | 8.100000 | 0.950 | 0.0 | 0.8 | -0.246858 | -0.073722 | 4.562450 | 3.736774 | 3.736774 | 0.0000 | ... | 1.52179 | 1.58106 | -0.24622 | 2.831645 | 0.566424 | 5454.735418 | 5.369136 | NaN | 0.60046 | 0.96991 |
| 8 | 8.100000 | 0.900 | 0.0 | 0.8 | -0.358181 | -0.098047 | 4.587620 | 3.721106 | 3.721106 | 0.0000 | ... | 1.65187 | 1.71735 | -0.26414 | 2.641553 | 0.438348 | 5261.455359 | 5.647442 | NaN | 0.65401 | 1.06566 |
| 9 | 8.100000 | 0.850 | 0.0 | 0.8 | -0.474711 | -0.121780 | 4.610261 | 3.703839 | 3.703839 | 0.0000 | ... | 1.80349 | 1.87674 | -0.30101 | 2.349644 | 0.335189 | 5056.377615 | 5.938767 | NaN | 0.71601 | 1.17693 |
| 10 | 8.100000 | 0.800 | 0.0 | 0.8 | -0.597804 | -0.145327 | 4.631026 | 3.684840 | 3.684840 | 0.0000 | ... | 1.97991 | 2.06267 | -0.34286 | 1.881039 | 0.252462 | 4839.938464 | 6.246499 | NaN | 0.78762 | 1.30597 |
| 11 | 8.100000 | 0.750 | 0.0 | 0.8 | -0.728511 | -0.169368 | 4.651080 | 3.664184 | 3.664184 | 0.0000 | ... | 2.19008 | 2.28384 | -0.40592 | 1.075974 | 0.186848 | 4615.127249 | 6.573268 | NaN | 0.87372 | 1.46057 |
| 12 | 8.100000 | 0.700 | 0.0 | 0.8 | -0.864977 | -0.195221 | 4.672823 | 3.642994 | 3.642994 | 0.0000 | ... | 2.40327 | 2.51537 | -0.46759 | -0.421246 | 0.136466 | 4395.352697 | 6.914432 | NaN | 0.96956 | 1.61149 |
| 13 | 8.100000 | 0.650 | 0.0 | 0.8 | -1.012628 | -0.226149 | 4.702494 | 3.621545 | 3.621545 | 0.0000 | ... | 2.57682 | 2.71848 | -0.55648 | -inf | 0.097134 | 4183.549674 | 7.283560 | NaN | 1.06201 | 1.72285 |
| 14 | 8.100000 | 0.600 | 0.0 | 0.8 | -1.152988 | -0.260413 | 4.736260 | 3.603587 | 3.603587 | 0.0000 | ... | 2.69514 | 2.86366 | -0.61492 | -inf | 0.070309 | 4014.089117 | 7.634459 | NaN | 1.13691 | 1.79490 |
| 15 | 8.100000 | 0.550 | 0.0 | 0.8 | -1.268473 | -0.291167 | 4.759979 | 3.590093 | 3.590093 | 0.0000 | ... | 2.78150 | 2.96999 | -0.68095 | -inf | 0.053892 | 3891.281506 | 7.923174 | NaN | 1.19831 | 1.84652 |
| 16 | 8.100000 | 0.500 | 0.0 | 0.8 | -1.414063 | -0.329963 | 4.796178 | 3.573093 | 3.573093 | 0.0000 | ... | 2.90841 | 3.11558 | -0.81893 | -inf | 0.038542 | 3741.906660 | 8.287148 | NaN | 1.29024 | 1.92579 |
| 17 | 8.100000 | 0.450 | 0.0 | 0.8 | -1.560148 | -0.373196 | 4.836887 | 3.558189 | 3.558189 | 0.0000 | ... | 3.03013 | 3.24735 | -0.92722 | -inf | 0.027533 | 3615.667953 | 8.652360 | NaN | 1.37750 | 2.00427 |
| 18 | 8.100000 | 0.400 | 0.0 | 0.8 | -1.690418 | -0.414652 | 4.868646 | 3.546349 | 3.546349 | 0.0000 | ... | 3.12717 | 3.34991 | -1.00765 | -inf | 0.020398 | 3518.427448 | 8.978036 | NaN | 1.44727 | 2.06791 |
| 19 | 8.100000 | 0.350 | 0.0 | 0.8 | -1.810935 | -0.454479 | 4.890308 | 3.536133 | 3.536133 | 0.0000 | ... | 3.21378 | 3.44106 | -1.10319 | -inf | 0.015455 | 3436.631053 | 9.279328 | NaN | 1.50979 | 2.12549 |
| 20 | 8.100000 | 0.300 | 0.0 | 0.8 | -1.925703 | -0.496960 | 4.908324 | 3.528682 | 3.528682 | 0.0000 | ... | 3.27957 | 3.51041 | -1.18325 | -inf | 0.011866 | 3378.171160 | 9.566247 | NaN | 1.55754 | 2.16952 |
| 21 | 8.100000 | 0.250 | 0.0 | 0.8 | -2.050513 | -0.543744 | 4.922710 | 3.520871 | 3.520871 | 0.0000 | ... | 3.35311 | 3.58805 | -1.28022 | -inf | 0.008902 | 3317.959343 | 9.878273 | NaN | 1.61108 | 2.21929 |
| 22 | 8.100000 | 0.200 | 0.0 | 0.8 | -2.201137 | -0.599432 | 4.937177 | 3.511059 | 3.511059 | 0.0000 | ... | 3.45150 | 3.69248 | -1.43388 | -inf | 0.006293 | 3243.838470 | 10.254833 | NaN | 1.68260 | 2.28711 |
| 23 | 8.100000 | 0.150 | 0.0 | 0.8 | -2.395952 | -0.669096 | 4.951565 | 3.497187 | 3.497187 | 0.0000 | ... | 3.60048 | 3.85088 | -1.73221 | -inf | 0.004018 | 3141.861887 | 10.741871 | NaN | 1.79121 | 2.39226 |
| 24 | 8.100000 | 0.100 | 0.0 | 0.8 | -2.689492 | -0.761060 | 4.959401 | 3.469784 | 3.469784 | 0.0000 | ... | 3.95060 | 4.21788 | -2.47892 | -inf | 0.002044 | 2949.742462 | 11.475720 | NaN | 2.05097 | 2.64853 |
| 25 | 8.100371 | 0.095 | 0.0 | 0.8 | -2.790000 | -0.806875 | 5.028756 | 3.467000 | 3.467000 | 2.7736 | ... | 4.21800 | 4.55000 | -1.95000 | -0.696971 | 0.001622 | 2930.893245 | 6.772189 | -3.58 | 2.25000 | 2.83800 |
| 26 | 8.100371 | 0.090 | 0.0 | 0.8 | -2.980000 | -0.863279 | 5.118083 | 3.449000 | 3.449000 | 2.7592 | ... | 4.37500 | 4.73000 | -2.37000 | 0.623478 | 0.001047 | 2811.900830 | 6.913199 | -4.13 | 2.34000 | 2.94500 |
| 27 | 8.100371 | 0.085 | 0.0 | 0.8 | -3.100000 | -0.899629 | 5.165960 | 3.438000 | 3.438000 | 2.7504 | ... | 4.53900 | 4.90000 | -2.70000 | 3.131805 | 0.000794 | 2741.574172 | 7.004074 | -4.62 | 2.45000 | 3.05900 |
| 28 | 8.100371 | 0.080 | 0.0 | 0.8 | -3.240000 | -0.935542 | 5.211456 | 3.420000 | 3.420000 | 2.7360 | ... | 4.78000 | 5.20000 | -3.15000 | 3.299591 | 0.000575 | 2630.267992 | 7.093855 | -5.32 | 2.58000 | 3.23000 |
| 29 | 8.100371 | 0.077 | 0.0 | 0.8 | -3.470000 | -0.982967 | 5.289706 | 3.384000 | 3.384000 | 2.7072 | ... | 5.34000 | 5.81000 | -3.09000 | 3.300000 | 0.000339 | 2421.029047 | 7.212417 | -5.78 | 3.06000 | 3.75000 |
| 30 | 8.100371 | 0.074 | 0.0 | 0.8 | -3.600000 | -0.991400 | 5.289313 | 3.356000 | 3.356000 | 2.6848 | ... | 5.55000 | 6.00000 | -3.45000 | 3.300000 | 0.000251 | 2269.864852 | 7.233500 | -6.25 | 3.11000 | 3.87000 |
31 rows × 64 columns
SPOTS_expanded_A_Li['34'][0.126]
| logAge | Mass | Fspot | Xspot | log(L/Lsun) | log(R/Rsun) | log(g) | log(Teff) | log(T_hot) | log(T_cool) | ... | G-H | G-K | G-V | A(Li) | Lsun | Teff | M_bol | BCv | RP-J | RP-H | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 8.100000 | 1.300 | 0.339 | 0.8 | 0.412254 | 0.125899 | 4.299428 | 3.801741 | 3.825969 | 3.729059 | ... | 1.309567 | 1.362724 | -0.139495 | 3.276616 | 2.583771 | 6334.923139 | 3.721355 | NaN | 0.518566 | 0.837763 |
| 1 | 8.100000 | 1.250 | 0.339 | 0.8 | 0.330256 | 0.105689 | 4.322815 | 3.791347 | 3.815575 | 3.718665 | ... | 1.389840 | 1.446442 | -0.141702 | 3.267539 | 2.139224 | 6185.104762 | 3.926349 | NaN | 0.550429 | 0.895707 |
| 2 | 8.100000 | 1.200 | 0.339 | 0.8 | 0.243977 | 0.082324 | 4.351816 | 3.781460 | 3.805687 | 3.708777 | ... | 1.468213 | 1.528737 | -0.142472 | 3.255346 | 1.753789 | 6045.883439 | 4.142047 | NaN | 0.581605 | 0.952059 |
| 3 | 8.100000 | 1.150 | 0.339 | 0.8 | 0.153256 | 0.056658 | 4.384665 | 3.771612 | 3.795840 | 3.698930 | ... | 1.548645 | 1.612523 | -0.141929 | 3.239359 | 1.423167 | 5910.339933 | 4.368851 | NaN | 0.613361 | 1.010532 |
| 4 | 8.100000 | 1.100 | 0.339 | 0.8 | 0.058102 | 0.029655 | 4.419364 | 3.761325 | 3.785553 | 3.688643 | ... | 1.633607 | 1.702145 | -0.137937 | 3.219617 | 1.143146 | 5771.983488 | 4.606736 | NaN | 0.647096 | 1.071900 |
| 5 | 8.100000 | 1.050 | 0.339 | 0.8 | -0.041363 | 0.001673 | 4.455125 | 3.750450 | 3.774677 | 3.677767 | ... | 1.725300 | 1.798901 | -0.133883 | 3.194028 | 0.909154 | 5629.242268 | 4.855397 | NaN | 0.683571 | 1.138128 |
| 6 | 8.100000 | 1.000 | 0.339 | 0.8 | -0.144951 | -0.026781 | 4.490844 | 3.738780 | 3.763008 | 3.666098 | ... | 1.831826 | 1.910695 | -0.147280 | 3.159421 | 0.716224 | 5479.993836 | 5.114368 | NaN | 0.726261 | 1.216717 |
| 7 | 8.100000 | 0.950 | 0.339 | 0.8 | -0.252749 | -0.054619 | 4.524244 | 3.725750 | 3.749977 | 3.653067 | ... | 1.949262 | 2.035408 | -0.159072 | 3.110233 | 0.558793 | 5318.017351 | 5.383863 | NaN | 0.775437 | 1.303649 |
| 8 | 8.100000 | 0.900 | 0.339 | 0.8 | -0.364868 | -0.081453 | 4.554431 | 3.711137 | 3.735364 | 3.638454 | ... | 2.076463 | 2.173704 | -0.159854 | 3.039105 | 0.431650 | 5142.056686 | 5.664161 | NaN | 0.833022 | 1.394801 |
| 9 | 8.100000 | 0.850 | 0.339 | 0.8 | -0.481992 | -0.107274 | 4.581249 | 3.694766 | 3.718994 | 3.622084 | ... | 2.201761 | 2.316690 | -0.163635 | 2.929287 | 0.329615 | 4951.834257 | 5.956971 | NaN | 0.896213 | 1.479903 |
| 10 | 8.100000 | 0.800 | 0.339 | 0.8 | -0.605662 | -0.132469 | 4.605310 | 3.676446 | 3.700674 | 3.603764 | ... | 2.333238 | 2.467459 | -0.192590 | 2.756017 | 0.247935 | 4747.295001 | 6.266145 | NaN | 0.966511 | 1.567894 |
| 11 | 8.100000 | 0.750 | 0.339 | 0.8 | -0.736951 | -0.157424 | 4.627192 | 3.656102 | 3.680329 | 3.583419 | ... | 2.480021 | 2.636019 | -0.230109 | 2.463906 | 0.183252 | 4530.035861 | 6.594368 | NaN | 1.052385 | 1.664184 |
| 12 | 8.100000 | 0.700 | 0.339 | 0.8 | -0.876848 | -0.181250 | 4.644880 | 3.633040 | 3.657268 | 3.560358 | ... | 2.676434 | 2.848968 | -0.299951 | 1.935785 | 0.132786 | 4295.761882 | 6.944110 | NaN | 1.168863 | 1.797669 |
| 13 | 8.100000 | 0.650 | 0.339 | 0.8 | -1.031144 | -0.210744 | 4.671684 | 3.609213 | 3.633441 | 3.536531 | ... | 2.878926 | 3.067116 | -0.360417 | 0.878639 | 0.093080 | 4066.430555 | 7.329850 | NaN | 1.295745 | 1.932833 |
| 14 | 8.100000 | 0.600 | 0.339 | 0.8 | -1.153239 | -0.239203 | 4.693838 | 3.592919 | 3.617146 | 3.520236 | ... | 3.007821 | 3.209468 | -0.431123 | -1.097940 | 0.070269 | 3916.686310 | 7.635088 | NaN | 1.384030 | 2.017734 |
| 15 | 8.100000 | 0.550 | 0.339 | 0.8 | -1.270122 | -0.264206 | 4.706057 | 3.576200 | 3.600427 | 3.503517 | ... | 3.138821 | 3.357220 | -0.474141 | -inf | 0.053688 | 3768.772046 | 7.927295 | NaN | 1.479751 | 2.106127 |
| 16 | 8.100000 | 0.500 | 0.339 | 0.8 | -1.440761 | -0.304085 | 4.744422 | 3.553480 | 3.577707 | 3.480797 | ... | 3.339630 | 3.580592 | -0.606917 | -inf | 0.036244 | 3576.675831 | 8.353893 | NaN | 1.632056 | 2.245813 |
| 17 | 8.100000 | 0.450 | 0.339 | 0.8 | -1.598062 | -0.348402 | 4.787298 | 3.536313 | 3.560540 | 3.463630 | ... | 3.511515 | 3.764531 | -0.716838 | -inf | 0.025231 | 3438.053813 | 8.747145 | NaN | 1.762608 | 2.370337 |
| 18 | 8.100000 | 0.400 | 0.339 | 0.8 | -1.732767 | -0.389883 | 4.819107 | 3.523377 | 3.547604 | 3.450694 | ... | 3.647991 | 3.907245 | -0.775352 | -inf | 0.018503 | 3337.158468 | 9.083908 | NaN | 1.867995 | 2.472091 |
| 19 | 8.100000 | 0.350 | 0.339 | 0.8 | -1.855306 | -0.429144 | 4.839638 | 3.512373 | 3.536600 | 3.439690 | ... | 3.765830 | 4.029448 | -0.853239 | -inf | 0.013954 | 3253.664509 | 9.390254 | NaN | 1.960479 | 2.561805 |
| 20 | 8.100000 | 0.300 | 0.339 | 0.8 | -1.970007 | -0.470971 | 4.856346 | 3.504611 | 3.528839 | 3.431929 | ... | 3.850802 | 4.117042 | -0.921175 | -inf | 0.010715 | 3196.032449 | 9.677009 | NaN | 2.027444 | 2.626882 |
| 21 | 8.100000 | 0.250 | 0.339 | 0.8 | -2.093052 | -0.516734 | 4.868690 | 3.496731 | 3.520959 | 3.424049 | ... | 3.943995 | 4.212080 | -1.002123 | -inf | 0.008071 | 3138.565164 | 9.984621 | NaN | 2.101488 | 2.698895 |
| 22 | 8.100000 | 0.200 | 0.339 | 0.8 | -2.239438 | -0.570778 | 4.879868 | 3.487157 | 3.511384 | 3.414474 | ... | 4.045065 | 4.315199 | -1.144710 | -inf | 0.005762 | 3070.130315 | 10.350584 | NaN | 2.179020 | 2.774489 |
| 23 | 8.100000 | 0.150 | 0.339 | 0.8 | -2.429194 | -0.639035 | 4.891443 | 3.473846 | 3.498074 | 3.401164 | ... | 4.146379 | 4.419486 | -1.452944 | -inf | 0.003722 | 2977.461880 | 10.824976 | NaN | 2.248586 | 2.842472 |
| 24 | 8.100000 | 0.100 | 0.339 | 0.8 | -2.712418 | -0.730690 | 4.898662 | 3.448868 | 3.473095 | 3.376185 | ... | 4.334799 | 4.613860 | -2.225280 | 0.663375 | 0.001939 | 2811.043719 | 11.533036 | NaN | 2.375223 | 2.967512 |
| 25 | 8.100371 | 0.085 | 0.340 | 0.8 | -3.100000 | -0.899629 | 5.165960 | 3.438000 | 3.438000 | 2.750400 | ... | 4.539000 | 4.900000 | -2.700000 | 3.131388 | 0.000794 | 2741.574172 | 7.004074 | -4.62 | 2.450000 | 3.059000 |
| 26 | 8.100371 | 0.080 | 0.340 | 0.8 | -3.240000 | -0.935542 | 5.211456 | 3.420000 | 3.420000 | 2.736000 | ... | 4.780000 | 5.200000 | -3.150000 | 3.299590 | 0.000575 | 2630.267992 | 7.093855 | -5.32 | 2.580000 | 3.230000 |
| 27 | 8.100371 | 0.077 | 0.340 | 0.8 | -3.470000 | -0.982967 | 5.289706 | 3.384000 | 3.384000 | 2.707200 | ... | 5.340000 | 5.810000 | -3.090000 | 3.300000 | 0.000339 | 2421.029047 | 7.212417 | -5.78 | 3.060000 | 3.750000 |
| 28 | 8.100371 | 0.074 | 0.340 | 0.8 | -3.600000 | -0.991400 | 5.289313 | 3.356000 | 3.356000 | 2.684800 | ... | 5.550000 | 6.000000 | -3.450000 | 3.300000 | 0.000251 | 2269.864852 | 7.233500 | -6.25 | 3.110000 | 3.870000 |
29 rows × 64 columns
SPOTS_expanded_A_Li['85'][0.126]
| logAge | Mass | Fspot | Xspot | log(L/Lsun) | log(R/Rsun) | log(g) | log(Teff) | log(T_hot) | log(T_cool) | ... | G-H | G-K | G-V | A(Li) | Lsun | Teff | M_bol | BCv | RP-J | RP-H | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 8.100000 | 1.300 | 0.847 | 0.8 | 0.412044 | 0.152254 | 4.246718 | 3.788511 | 3.863769 | 3.766859 | ... | 1.251268 | 1.297885 | -0.164622 | 3.295477 | 2.582520 | 6144.850624 | 3.721881 | NaN | 0.493029 | 0.782041 |
| 1 | 8.100000 | 1.250 | 0.847 | 0.8 | 0.330066 | 0.148334 | 4.237524 | 3.769977 | 3.845234 | 3.748324 | ... | 1.392416 | 1.445444 | -0.187981 | 3.293454 | 2.138289 | 5888.121595 | 3.926824 | NaN | 0.550479 | 0.883542 |
| 2 | 8.100000 | 1.200 | 0.847 | 0.8 | 0.243698 | 0.131549 | 4.253365 | 3.756777 | 3.832035 | 3.735125 | ... | 1.496317 | 1.554558 | -0.198745 | 3.290561 | 1.752660 | 5711.855868 | 4.142746 | NaN | 0.593145 | 0.959245 |
| 3 | 8.100000 | 1.150 | 0.847 | 0.8 | 0.152666 | 0.109666 | 4.278648 | 3.744961 | 3.820219 | 3.723309 | ... | 1.593497 | 1.656745 | -0.207143 | 3.286534 | 1.421237 | 5558.543901 | 4.370324 | NaN | 0.633087 | 1.030495 |
| 4 | 8.100000 | 1.100 | 0.847 | 0.8 | 0.056709 | 0.083544 | 4.311586 | 3.734032 | 3.809290 | 3.712380 | ... | 1.685921 | 1.754081 | -0.213987 | 3.280966 | 1.139485 | 5420.412773 | 4.610219 | NaN | 0.670967 | 1.098372 |
| 5 | 8.100000 | 1.050 | 0.847 | 0.8 | -0.044366 | 0.054240 | 4.349991 | 3.723416 | 3.798673 | 3.701763 | ... | 1.779796 | 1.852219 | -0.227984 | 3.273507 | 0.902888 | 5289.511674 | 4.862905 | NaN | 0.708963 | 1.167553 |
| 6 | 8.100000 | 1.000 | 0.847 | 0.8 | -0.150509 | 0.023071 | 4.391139 | 3.712464 | 3.787722 | 3.690812 | ... | 1.879778 | 1.957670 | -0.237135 | 3.263712 | 0.707116 | 5157.797769 | 5.128263 | NaN | 0.749261 | 1.240517 |
| 7 | 8.100000 | 0.950 | 0.847 | 0.8 | -0.261217 | -0.009255 | 4.433516 | 3.700951 | 3.776208 | 3.679298 | ... | 1.985664 | 2.069572 | -0.246427 | 3.252158 | 0.548003 | 5022.854178 | 5.405033 | NaN | 0.791848 | 1.317301 |
| 8 | 8.100000 | 0.900 | 0.847 | 0.8 | -0.375724 | -0.041868 | 4.475261 | 3.688631 | 3.763888 | 3.666978 | ... | 2.113048 | 2.202842 | -0.267726 | 3.239739 | 0.420995 | 4882.368444 | 5.691299 | NaN | 0.843191 | 1.411642 |
| 9 | 8.100000 | 0.850 | 0.847 | 0.8 | -0.494575 | -0.073767 | 4.514235 | 3.674867 | 3.750125 | 3.653215 | ... | 2.256260 | 2.354777 | -0.292595 | 3.225745 | 0.320202 | 4730.064360 | 5.988428 | NaN | 0.903835 | 1.516767 |
| 10 | 8.100000 | 0.800 | 0.847 | 0.8 | -0.619289 | -0.104438 | 4.549249 | 3.659024 | 3.734282 | 3.637372 | ... | 2.409829 | 2.522965 | -0.304589 | 3.201491 | 0.240276 | 4560.623177 | 6.300213 | NaN | 0.975957 | 1.624301 |
| 11 | 8.100000 | 0.750 | 0.847 | 0.8 | -0.749999 | -0.133807 | 4.579957 | 3.641031 | 3.716288 | 3.619378 | ... | 2.555480 | 2.693391 | -0.337469 | 3.157947 | 0.177828 | 4375.532359 | 6.626987 | NaN | 1.054013 | 1.717890 |
| 12 | 8.100000 | 0.700 | 0.847 | 0.8 | -0.893310 | -0.162198 | 4.606777 | 3.619399 | 3.694656 | 3.597746 | ... | 2.702442 | 2.871119 | -0.380797 | 3.080684 | 0.127847 | 4162.927620 | 6.985266 | NaN | 1.145778 | 1.809118 |
| 13 | 8.100000 | 0.650 | 0.847 | 0.8 | -1.035662 | -0.190024 | 4.630243 | 3.597724 | 3.672981 | 3.576071 | ... | 2.854556 | 3.051273 | -0.470558 | 2.935574 | 0.092117 | 3960.258456 | 7.341146 | NaN | 1.254237 | 1.902633 |
| 14 | 8.100000 | 0.600 | 0.847 | 0.8 | -1.103566 | -0.197423 | 4.610279 | 3.584447 | 3.659705 | 3.562795 | ... | 2.967135 | 3.174638 | -0.534171 | 2.641929 | 0.078783 | 3841.026027 | 7.510906 | NaN | 1.332758 | 1.976496 |
| 15 | 8.100000 | 0.550 | 0.847 | 0.8 | -1.289990 | -0.212640 | 4.602925 | 3.545450 | 3.620707 | 3.523797 | ... | 3.299838 | 3.529129 | -0.728217 | 1.942069 | 0.051287 | 3511.151994 | 7.976966 | NaN | 1.574925 | 2.197647 |
| 16 | 8.100000 | 0.500 | 0.847 | 0.8 | -1.501147 | -0.253464 | 4.643180 | 3.513072 | 3.588330 | 3.491420 | ... | 3.596756 | 3.851911 | -0.991065 | 0.048188 | 0.031539 | 3258.910549 | 8.504858 | NaN | 1.803036 | 2.404132 |
| 17 | 8.100000 | 0.450 | 0.847 | 0.8 | -1.671891 | -0.297670 | 4.685835 | 3.492490 | 3.567747 | 3.470837 | ... | 3.849174 | 4.120597 | -1.195125 | -inf | 0.021287 | 3108.062046 | 8.931716 | NaN | 1.994780 | 2.588993 |
| 18 | 8.100000 | 0.400 | 0.847 | 0.8 | -1.809499 | -0.338319 | 4.715980 | 3.478412 | 3.553670 | 3.456760 | ... | 4.030810 | 4.311352 | -1.260680 | -inf | 0.015506 | 3008.929900 | 9.275738 | NaN | 2.135427 | 2.727151 |
| 19 | 8.100000 | 0.350 | 0.847 | 0.8 | -1.931932 | -0.376595 | 4.734541 | 3.466942 | 3.542199 | 3.445289 | ... | 4.190163 | 4.476416 | -1.302754 | -inf | 0.011697 | 2930.501311 | 9.581821 | NaN | 2.261292 | 2.851288 |
| 20 | 8.100000 | 0.300 | 0.847 | 0.8 | -2.049000 | -0.416230 | 4.746863 | 3.457492 | 3.532750 | 3.435840 | ... | 4.322062 | 4.612402 | -1.356427 | -inf | 0.008933 | 2867.426272 | 9.874489 | NaN | 2.367223 | 2.955991 |
| 21 | 8.100000 | 0.250 | 0.847 | 0.8 | -2.172126 | -0.459132 | 4.753487 | 3.448162 | 3.523419 | 3.426509 | ... | 4.459973 | 4.753352 | -1.426080 | -inf | 0.006728 | 2806.479796 | 10.182305 | NaN | 2.478958 | 3.066405 |
| 22 | 8.100000 | 0.200 | 0.847 | 0.8 | -2.314691 | -0.509727 | 4.757766 | 3.437818 | 3.513075 | 3.416165 | ... | 4.596780 | 4.891715 | -1.554233 | -inf | 0.004845 | 2740.425311 | 10.538718 | NaN | 2.589561 | 3.175693 |
| 23 | 8.100000 | 0.150 | 0.847 | 0.8 | -2.497531 | -0.574975 | 4.763323 | 3.424732 | 3.499990 | 3.403080 | ... | 4.660562 | 4.956105 | -1.866302 | -inf | 0.003180 | 2659.084019 | 10.995818 | NaN | 2.637642 | 3.223192 |
| 24 | 8.100000 | 0.100 | 0.847 | 0.8 | -2.761755 | -0.665781 | 4.768844 | 3.404079 | 3.479336 | 3.382426 | ... | 4.693072 | 4.989016 | -2.516235 | 3.298338 | 0.001731 | 2535.588947 | 11.656379 | NaN | 2.656696 | 3.242057 |
| 25 | 8.100371 | 0.077 | 0.850 | 0.8 | -3.470000 | -0.982967 | 5.289706 | 3.384000 | 3.384000 | 2.707200 | ... | 5.340000 | 5.810000 | -3.090000 | 3.300000 | 0.000339 | 2421.029047 | 7.212417 | -5.78 | 3.060000 | 3.750000 |
| 26 | 8.100371 | 0.074 | 0.850 | 0.8 | -3.600000 | -0.991400 | 5.289313 | 3.356000 | 3.356000 | 2.684800 | ... | 5.550000 | 6.000000 | -3.450000 | 3.300000 | 0.000251 | 2269.864852 | 7.233500 | -6.25 | 3.110000 | 3.870000 |
27 rows × 64 columns
fig, ax = plt.subplots(1, 1, figsize=(8, 6))
ax.set_ylabel('$A(Li)$ [dex]')
ax.set_xlabel('G-RP [mag]')
ax.plot(SPOTS_edr3['00'][0.126]['G_abs'] - SPOTS_edr3['00'][0.126]['RP_abs'], SPOTS_edr3['00'][0.126]['A(Li)'], linewidth=1, label='SPOTS f000')
ax.plot(SPOTS_edr3['17'][0.126]['G_abs'] - SPOTS_edr3['17'][0.126]['RP_abs'], SPOTS_edr3['00'][0.126]['A(Li)'], linewidth=1, label='SPOTS f017')
ax.plot(SPOTS_edr3['51'][0.126]['G_abs'] - SPOTS_edr3['51'][0.126]['RP_abs'], SPOTS_edr3['00'][0.126]['A(Li)'], linewidth=1, label='SPOTS f051')
#ax.scatter(SPOTS_edr3_expanded['00'][0.126]['G-RP'], SPOTS_edr3_expanded['00'][0.126]['A(Li)'], s=10, zorder=4, label='SPOTS expanded')
ax.plot(SPOTS_expanded['00'][0.126]['G-RP'], SPOTS_expanded['00'][0.126]['A(Li)'], lw=1, ls=':', color='b', zorder=4, label='SPOTS expanded')
ax.plot(SPOTS_expanded_A_Li['00'][0.126]['G-RP'], SPOTS_expanded_A_Li['00'][0.126]['A(Li)'], lw=1, ls=':', color='r', zorder=4, label='SPOTS expanded')
ax.plot(BTSettl_Li_isochrones[0.120]['G_abs'] - BTSettl_Li_isochrones[0.120]['RP_abs'], BTSettl_Li_isochrones[0.120]['A(Li)'], linewidth=1, label='BT-Settl', linestyle='--', color='k')
#ax.errorbar(data_obs_Pleiades['G_abs'] - data_obs_Pleiades['RP_abs'], data_obs_Pleiades['ALi'], yerr=data_obs_Pleiades['e_ALi'], fmt='.', zorder=0, color='r', elinewidth=1, capsize=2)
fig.legend(fontsize=12, loc='lower center', ncol=3, bbox_to_anchor=(0.525, -0.1))
<matplotlib.legend.Legend at 0x7f0052bcb350>
fig, ax = plt.subplots(1, 1, figsize=(8, 6))
ax.set_ylabel('$A(Li)$ [dex]')
ax.set_xlabel('G-J [mag]')
ax.plot(SPOTS_edr3['00'][0.126]['G_abs'] - SPOTS_edr3['00'][0.126]['J_mag'], SPOTS_edr3['00'][0.126]['A(Li)'], linewidth=1, label='SPOTS f000')
ax.plot(SPOTS_edr3['17'][0.126]['G_abs'] - SPOTS_edr3['17'][0.126]['J_mag'], SPOTS_edr3['00'][0.126]['A(Li)'], linewidth=1, label='SPOTS f017')
ax.plot(SPOTS_edr3['34'][0.126]['G_abs'] - SPOTS_edr3['34'][0.126]['J_mag'], SPOTS_edr3['00'][0.126]['A(Li)'], linewidth=1, label='SPOTS f034')
ax.plot(SPOTS_edr3['51'][0.126]['G_abs'] - SPOTS_edr3['51'][0.126]['J_mag'], SPOTS_edr3['00'][0.126]['A(Li)'], linewidth=1, label='SPOTS f051')
#ax.scatter(SPOTS_edr3_expanded['00'][0.126]['G-RP'], SPOTS_edr3_expanded['00'][0.126]['A(Li)'], s=10, zorder=4, label='SPOTS expanded')
ax.plot(SPOTS_expanded['00'][0.126]['G-J'], SPOTS_expanded['00'][0.126]['A(Li)'], lw=1, ls=':', color='b', zorder=4, label='SPOTS expanded')
ax.plot(BTSettl_Li_isochrones[0.120]['G_abs'] - BTSettl_Li_isochrones[0.120]['J_abs'], BTSettl_Li_isochrones[0.120]['A(Li)'], linewidth=1, label='BT-Settl', linestyle='--', color='k')
#ax.errorbar(data_obs_Pleiades['G_abs'] - data_obs_Pleiades['J_abs'], data_obs_Pleiades['ALi'], yerr=data_obs_Pleiades['e_ALi'], fmt='.', zorder=0, color='r', elinewidth=1, capsize=2)
ax.set_xlim(0, 6)
fig.legend(fontsize=12, loc='lower center', ncol=3, bbox_to_anchor=(0.525, -0.1))
<matplotlib.legend.Legend at 0x7f008479c1d0>
#SPOTS_extended(MS_color_file, BTSettl, SPOTS, f).plot_CMD_ALi(SPOTS_expanded, BTSettl_Li_isochrones_Teff_dic, BTSettl_Li_isochrones, data_obs_Pleiades)
ages_SPOTS = np.array([float(age) for age in SPOTS_edr3['00'].keys()])
def plot_SPOTS_data(age_array, f_array, band1, band2, bandobs):
"""
Plot SPOTS data with customizable age, f, and band combinations.
Parameters:
age_array (array-like): array of age values in Gyr.
f_array (array-like): array of f values.
band1 (str): first band for plotting.
band2 (str): second band for plotting.
bandobs (str): band for observational data.
"""
plt.rcParams.update({'font.size': 12}) # Set the font size
fig, ax = plt.subplots(1, 1, figsize=(8, 6))
ax.set_ylabel(f'{band1} [mag]')
ax.set_xlabel(f'{band1}-{band2} [mag]')
# Color map creation
cmap = plt.get_cmap('tab10')
colors = [cmap(i) for i in np.linspace(0, 1, len(age_array))]
# Line style array
ls_array = ['-', '--', '-.', ':']
for i, age in enumerate(age_array):
age_Myr = age * 1000
for j, f in enumerate(f_array):
closest_age = ages_SPOTS[np.abs(ages_SPOTS - age).argmin()]
if closest_age in SPOTS_edr3[f]:
ax.plot(
SPOTS_edr3[f][closest_age][f'{band1}_abs'] - SPOTS_edr3[f][closest_age][f'{band2}_abs'],
SPOTS_edr3[f][closest_age][f'{band1}_abs'],
label=f'SPOTS-YBC f0{f}; {age_Myr} Myr',
color=colors[i],
lw=1,
ls=ls_array[j % len(ls_array)]
)
else:
print(f"Closest age {closest_age} not found in SPOTS_edr3[f][{f}]")
ax.scatter(data_obs_Pleiades[f'{band1}_abs'] - data_obs_Pleiades[f'{band2}_abs'], data_obs_Pleiades[f'{bandobs}_abs'], s=10, zorder=0, color='r', alpha=0.125)
ax.legend(fontsize=10, loc='upper center', bbox_to_anchor=(1.25, 0.9))
ax.invert_yaxis()
plt.show()
age_array = [0.001, 0.02, 0.120, 0.6, 4]
f_array = ['00', '51', '85']
plot_SPOTS_data(age_array, f_array, 'G', 'RP', 'G')
age_array = [0.001, 0.02, 0.120, 0.6, 4] f_array = ['00']
plot_SPOTS_data(age_array, f_array, 'G', 'RP', 'G')
plot_SPOTS_data(age_array, f_array, 'G', 'J', 'G')
Isochrones plots¶
plt.rcParams.update({'font.size': 14, 'axes.linewidth': 1, 'axes.edgecolor': 'k'})
plt.rcParams['font.family'] = 'serif'
Isochrones in HRD (interior) and CMD (atmosphere)
from models_test import Isochrones
bands1 = [['G_abs', 'RP_abs'], ['G_abs', 'J_abs'], ['J_abs', 'K_abs'], ['G_abs', 'y_abs'], ['G_abs', 'z_abs']]
bands2 = [['G_i00', 'G_RP_i00'], ['G_i00', 'J_i00'], ['J_i00', 'Ks_i00'], ['G_i00', 'yP1_i00'], ['G_i00', 'zP1_i00']]
bandsobs = [['G', 'RP'], ['G', 'J'], ['J', 'K'], ['G', 'y'], ['G', 'z']]
isochrones = [0.02, 0.08, 0.12, 0.6]
isochrones_grid = Isochrones(
BTSettl_Li_isochrones,
PARSEC_iso_omega_00_Phot_dict,
bands1,
bands2,
'BT-Settl $\odot$',
r'PARSEC $\omega_i=0.0$; $Z=0.001547$',
isochrones,
filename='model_comparison',
data_obs=data_obs_Pleiades,
obs=True,
bandsobs=bandsobs,
dpi=350
)
%matplotlib inline
isochrones_grid.plot_isochrones_grid()
%matplotlib inline
isochrones_grid.plot_single_column(['BP_abs', 'RP_abs', 'G_abs'], ['G_BP_i00', 'G_RP_i00', 'G_i00'], ['BP', 'RP', 'G'])